Efficient Resource Management at Pinterest’s Batch Processing Platform

1,420
Pinterest
Pinterest's profile on StackShare is not actively maintained, so the information here may be out of date.

Yongjun Zhang | Software Engineer, Ang Zhang | Engineering Manager, Shaowen Wang | Software Engineer, Batch Processing Platform Team


Pinterest’s Batch Processing Platform, Monarch, runs most of the batch processing workflows of the company. At the scale shown in Table 1, it is important to manage the platform resources to provide quality of service (QoS) while achieving cost efficiency. This article shares how we do that and future work.

alt_text

Table 1: Scale of Monarch Batch Processing Platform

Introduction of Monarch

Figure 1 shows what Pinterest’s data system looks like at a high level. When users are using Pinterest applications on their mobile or desktop devices, they generate various logs that are ingested to our system via Singer + Kafka (see Scalable and reliable data ingestion at Pinterest) and the resulting data is stored to S3. Then the data is processed and analyzed by various workflows like sanitization, analytics, and machine learning data preparation. The results of the workflows are typically stored back to S3. There are essentially two types of processing platforms: batch and streaming. This blog is about the batch processing platform named Monarch. See this blog for more information about the streaming platform.

As an in-house big data platform, Monarch provides the infrastructure, services, and tools to help users develop, build, deploy, and troubleshoot their batch processing applications (mostly in the form of workflows) at scale. Monarch consists of more than 20 Hadoop YARN clusters built entirely in the Cloud utilizing AWS EC2, and we use many different instance types offered by EC2. The actual EC2 instance type we employ at a cluster depends on its workload; some clusters are more optimized for computing, while others have more memory or disk capacity.

User workflows can be submitted to Monarch from Spinner (an internal workflow platform built on top of Airflow) and other UI based workflow orchestration tools via Job Submission Service, or JSS (see Figure 2). The user workflow source code typically specifies the cluster and queue in which the workflow should run.

alt_text

Figure 1. Pinterest Data System and the Batch Processing Platform (Monarch).

alt_text

Figure 2. Pinterest Job Submission Service. See more description in the text.

Resource Management Challenges

Hadoop YARN is used to manage the cluster resources and task scheduling. The cluster resources are represented as a tree of queues. All the resources of the cluster, or all the EC2 instances the cluster has, are represented as the “root” of the tree, and the leaf nodes of the tree are where applications run. The weight configuration of a queue determines the amount of resources allocated to it. Child nodes of the same parent node share the resources allocated to the parent. How much resource a child gets is based on the ratio of this child’s weight over the sum of the weights of all sibling nodes. By setting the node weight, we can control how many EC2 instances are assigned to any given queue. YARN supports multiple schedulers, and the Fair Scheduler is used in Monarch.

alt_text

Figure 3. YARN’s resource allocation: Tree of Queues of Adhoc Structure.

The goal of using a tree of queues to represent resource allocation is to achieve resource isolation between workflows that run in different queues. However, Monarch initially didn’t have a consistent queue structure, as shown in Figure 3. Some queues were allocated to specific projects, some were for organizations, and others for workflows of a certain priority. As a result, there was severe interference between different workflows running in the same queue — more critical workflows were often impacted by non-critical ones.

There were mainly two reasons for interference:

  1. Workflows running in the same queue are treated the same. With no notion of priority, the scheduler has no way to give more resources to more critical workflows.
  2. There is a parameter maxRunningApps to control how many applications can concurrently run in a given queue. This prevents too many applications competing for resources, in which situation no application can make good progress. However, if lower priority workflows are submitted first and saturate the maxRunningApps, then critical workflows submitted later can be stuck for a long time without being scheduled.

To address these issues, we introduced workflow tiering and changed the resource allocation queues to be tier-ed accordingly.

Workflow Tiering and Hierarchical Queue Structure

The workloads on Monarch are typically in the form of workflows. Workflow is represented as a Directed Acyclic Graph (DAG) of multiple jobs to process input data and generate output. The jobs in the same workflow run in parallel or sequentially depending on whether there is dependency on each other. We took two main steps to provide QoS for workflows while achieving cost efficiency.

Firstly, we added tiering to distinguish critical workflows from non-critical ones. The critical workflows typically have higher requirements on the finishing time. We decided to classify workflows into three tiers: tier1, tier2, and tier3 (tier1 has the highest importance). Then we worked with user teams to define the tiering and runtime service level objective (SLO) of all workflows that run on the Monarch platform.

Secondly, we changed the resource queue structure across all clusters to have the notion of tiering, project, and organization. Given that each workflow is associated with a project, each project belongs to a team, and each team belongs to a larger organization, we decided to create a three level hierarchical queue structure: organization, project, and tier. See Figure 4 for an example (“default” is used in place of tier3, for historical reason).

alt_text

Figure 4. Hierarchical Queues with Organization, Project and Tiering.

Some of the most important configurations of the queues are:

  • Weight: The weight of a queue determines the amount of resources allocated to it. Child nodes of the same parent node share the parent’s resources based on the relative ratio of their weights.
  • MaxRunningApps: The maximum number of applications that can run concurrently within the queue. This prevents from having too many applications running in the same queue of limited resources, meaning no applications can make good progress.
  • Preemption:
  1. preemption: whether to enable preemption
  2. fairSharePreemptionTimeout: number of seconds the queue is under its fair share threshold before it will try to preempt containers to take resources from other queues.
  3. fairSharePreemptionThreshold: the fair share preemption threshold for the queue. If the queue waits fairSharePreemptionTimeout without receiving fairSharePreemptionThreshold*fairShare resources, it is allowed to preempt containers to take resources from other queues.
  4. allowPreemptionFrom: determines whether the scheduler is allowed to preempt resources from the queue.

We configure tier1 queues to not allow preemption and also configure the other two parameters (fairSharePreemptionTimeout and fairSharePreemptionThreshold) to smaller values than for tier2 and tier3 queues. This allows tier1 queues to acquire resources faster when they are not getting their fair share of resources.

Because Monarch has many clusters, and the workflows running on different clusters could change from time to time, it’s not practical or efficient to manually create the queue structures. We developed a tool that analyzes the historical data of the workflows on the clusters, generates the queue structure, and updates the settings automatically and periodically.

Besides the preemption configuration described above, two of the most important configurations are the queue weight and maxRunningApps. In the next section, we will share more details on the algorithm we use to generate these settings.

Resource Allocation Algorithm

The workflows running in a queue have different requirements at different times. To ensure QoS of the critical workflows, we designed an algorithm to assign queue weight based on historical run data, namely, the Percentile Algorithm.

alt_text

Figure 5. The Percentile Resource Allocation Algorithm.

The algorithm looks at the historical run data within the most recent time window, such as 30 days, to see how much resource is needed for a given queue. Below is what it does:

  • Step 1: The queue may be used at some times and may be vacant at other times. When the queue is being used, sometimes X EC2 instances are being used and sometimes Y EC2 instances are being used. The algorithm divides the time window into time units; each unit is a timespan that the same number of EC2 instances are used. The time unit is represented as <timeLength, instanceUsed>. (See the left side of Figure 5)
  • Step 2: Excluding the time units in which the queue is vacant, sort the time units by the number of instances used in the time unit (see the right side of Figure 5) from smallest to largest.
  • Step 3: Determine the minimum number of instances to allocate to the queue to make sure a pre-specified time length percentage threshold is met. This threshold means, given the total length of time units (TTIU) that the queue is in use, the allocated resource needs to be enough to satisfy the percentage of TTIU. For example, for a tier1 queue that is used for 240 hours in total within a 30-day window (vacant other times), we’d like to guarantee the resources for 95% of the time, thus it’s 228 hours. The algorithm finds out the number of instances being used at the sorted results from Step 2. For example, tu0 + tu4 + tu7 + tu2 is about 95% of the whole time length in use, then the number of instances used in tu2 is the number of instances to be allocated to this queue. If we were to allocate the number of instances used in tu5, which is larger than used in tu2, it would potentially cause waste because tu2 is only 5% of the whole time the queue is in use.

The 95% threshold above is just an example. We evaluated the resource usage of different tiers and came up with different thresholds based on the size of the clusters and resources used by those workflows. The thresholds are also adjusted from time to time when the percentages of resources used by different tiers change.

There are several reasons we don’t have to guarantee 100% of the resources required at the peak usage time of a given tier1/tier2 queue, thus avoiding waste.

  1. The workflow tiering has a rough distribution such that ~10% workflows are tier1, 20–30% tier2, and 60–70% tier3.
  2. Not all queues are busy at the same time, and the YARN scheduler allows workflows to use resources available at other queues.
  3. Higher tier queues can preempt resources faster.

We measure the resource headroom of a queue by a metric called usage/capacity ratio. The capacity of a queue is the number of instances allocated to the queue times the length of the time window being measured. The usage is measured by YARN as instance-hours. E.g., if the queue uses X instances for Y hours, the resource usage is X * Y instance-hours. In addition, we also measure vcore-hours and memory-hours usage/capacity ratio in a similar fashion to see how balanced the vcore and memory resource usage is. Notice that YARN reported vcore-hours and memory-hours, and we use the dominant-resource (DR) method to calculate the instance-hours here.

The algorithm ensures the percentage threshold is set in a decreasing order from tier1 to tier3 queues, while it also ensures that the usage/capacity ratio is in an increasing order. This means the head room is the largest for tier1, second for tier2, and smallest for tier3.

The resource allocation algorithm also looks at historical run data to determine the maxRunningApps setting and sets this configuration with some headroom for each queue.

Comparing with Autoscaling

Autoscaling is another common approach to save cost in the Cloud, scaling up the cluster when needed and scaling down when peak demand has passed. Because Cloud providers normally charge much higher rates for on-demand capacity than reserved instances, users normally reserve the capacity that is always required and use on-demand instances for the autoscaling.

Autoscaling works well for online services at Pinterest, but we found it is not as cost efficient for batch processing for the following reasons:

  1. Tasks from large scale batch processing can run for hours, and the two options to scale down the cluster are wasteful. Scaling down gracefully and waiting for running tasks to finish (i.e. draining the instances before terminating them) potentially wastes a significant amount of resources because the instances may not be fully utilized. Scaling down by terminating instances forcefully even when tasks are still running on them means unfinished computing is wasted (and longer runtime for the involved jobs) and extra resources are needed to rerun the terminated tasks.
  2. In order for autoscaling using on-demand instances to make economical sense when compared with reserved instances, we estimated the percentage of time of peak consumption of the cluster using on-demand instances will need to be less than 30% for certain instance types. Considering the time it takes to scale down, the percentage would be a lot smaller. However, it’s hard to control this percentage, and resources can easily be wasted if the percentage goes higher.
  3. At Pinterest’s big data processing scale, using autoscaling would require getting hundreds or more instances of desired instance types during peak hours, which is not always possible. Not getting enough resources to run critical workflows could affect the business in a significant way.

By utilizing the resource allocation algorithm described above and workflow tiering, we were able to utilize good reserved instance pricing while still guaranteeing enough resources for critical workflows when needed.

Please note that in this blog, we focus on production workflows, not adhoc workloads like Spark SQL queries from Querybook or PySpark jobs from Jupyter notebooks. On adhoc clusters, we do utilize autoscaling with Spot instances because the peak usage only lasts 2–3 hours on business days.

Workflow Performance Monitoring

When allocating resources for a workflow, the runtime SLO is an important factor to consider. For example, if the workflow uses X instances-hours resources, and the runtime SLO is 12 hours, then the number of instances needed to run this workflow is X / 12.

With the resource allocation being in effect, we need a way to monitor the overall workflow runtime performance. We developed a dashboard to show how each tier workflows are performing in various clusters.

Within a time window of a certain size, for any given workflow, if it is run for X times and Y runs meet SLO, its SLO success ratio is defined as Y/X. It’s ideal if this ratio is 100% for any given workflow, but it’s not feasible for many reasons. As a compromise, we define a workflow as SLO-successful if its SLO success ratio is no less than 90%.

As mentioned earlier, we classified workflows into three tiers. For workflows of each tier, we measure the percentage of workflows that are SLO-successful. Our goal is to have this percentage higher than 90%.

Figure 6 is a snapshot of the dashboard that measures the performance of the 30-day time window. Before the project, the tier1 workflow’s success percentage was around 70%. It has been improved to and stabilized around 90% now. While we try to make most tier1 workflows successful, the same metrics of other tiers are not sacrificed too much because they have less stringent SLO requirements.

alt_text

Figure 6. Workflow performance monitoring: runtime SLO success ratio of each tier.

Cluster Resource Usage Monitoring

The workflow requirement is not static and may change from time to time. A daily report is done for each cluster on the following metrics:

  1. Total, tier1, tier2, and tier3 usage/capacity ratio (including instance, vcore, memory)
  2. Number of all tier1, tier2, and tier3 workflows running in the cluster (there may be new workflows onboarded, or re-tiering and SLO change of existing workflows)

Based on these metrics, we determine if the cluster is over or under utilized and take actions by either adding more resources to the cluster (organic growth), downsizing the cluster to save cost, or keeping it as is.

Cross-Cluster Routing And Load Balancing

As mentioned earlier, different workflows have different resource needs — some require more memory, some more CPU, and others more disk IO or storage. Their needs may change over time. Additionally, some clusters may become full while others are underutilized over time. Through monitoring resource consumption, we may find better home clusters for the workflows than their current ones. To ask users to change their source code to move the workflow is a tedious process, as we also have to adjust the resource allocation when we move the workflow.

We developed a cross-cluster routing (CCR) capability to change the target cluster of the workflows without the need of users to change settings. To implement this, we added instrumentation logic in the JSS component that can redirect jobs to another cluster as we need.

We also developed a workflow to periodically analyze the cluster usage and choose candidate workflows to move to other clusters to keep improving the load balancing and cost efficiency.

To enable redirecting jobs, we need to do resource allocation change on the target cluster with the above mentioned algorithm. To achieve this, we automated the resource allocation process such that with a single button click (triggering a workflow), it will do both resource allocation and configure job redirection in one step.

Current and Future Work

At the time of writing, our metrics indicate the vcore and memory usage of a fairly big cluster is not balanced, and a lot of vcores are wasted as a result. We are working on splitting this cluster into two clusters of different instance types with CCR support and migrating the workflows running on the original cluster into one of the resulting clusters. We expect with this change we will be able to not only run the applications more reliably, but also save a lot of cost.

Our clusters are located at different availability zones. When one zone has an issue, we can leverage the CCR feature to move critical workflows to another cluster in a different zone. We are working on making this process smoother.

We are also looking into dynamically route jobs at runtime to different clusters when the current load on the target cluster is full.

Acknowledgement

Thanks to Hengzhe Guo, Bogdan Pisica, Sandeep Kumar from the Batch Processing Platform team who helped further improve the implementations. Thanks to Soam Acharya, Jooseong Kim and Hannah Chen for driving the workflow tiering. Thanks to Jooseong Kim, William Tom, Soam Acharya, Chunyan Wang for the discussions and support along the way. Thanks to the workflow team, our platform user teams for their feedback and support.

Pinterest
Pinterest's profile on StackShare is not actively maintained, so the information here may be out of date.
Tools mentioned in article
Open jobs at Pinterest
Sr. Staff Software Engineer, Ads ML I...
San Francisco, CA, US; , CA, US
<div class="content-intro"><p><strong>About Pinterest</strong><span style="font-weight: 400;">:&nbsp;&nbsp;</span></p> <p>Millions of people across the world come to Pinterest to find new ideas every day. It’s where they get inspiration, dream about new possibilities and plan for what matters most. Our mission is to help those people find their inspiration and create a life they love.&nbsp;In your role, you’ll be challenged to take on work that upholds this mission and pushes Pinterest forward. You’ll grow as a person and leader in your field, all the while helping&nbsp;Pinners&nbsp;make their lives better in the positive corner of the internet.</p> <p>Creating a life you love also means finding a career that celebrates the unique perspectives and experiences that you bring. As you read through the expectations of the position, consider how your skills and experiences may complement the responsibilities of the role. We encourage you to think through your relevant and transferable skills from prior experiences.</p> <p><em>Our new progressive work model is called PinFlex, a term that’s uniquely Pinterest to describe our flexible approach to living and working. Visit our </em><a href="https://www.pinterestcareers.com/pinflex/" target="_blank"><em><u>PinFlex</u></em></a><em> landing page to learn more.&nbsp;</em></p></div><p>Pinterest is one of the fastest growing online advertising platforms. Continued success depends on the machine-learning systems, which crunch thousands of signals in a few hundred milliseconds, to identify the most relevant ads to show to pinners. You’ll join a talented team with high impact, which designs high-performance and efficient ML systems, in order to power the most critical and revenue-generating models at Pinterest.</p> <p><strong>What you’ll do</strong></p> <ul> <li>Being the technical leader of the Ads ML foundation evolution movement to 2x Pinterest revenue and 5x ad performance in next 3 years.</li> <li>Opportunities to use cutting edge ML technologies including GPU and LLMs to empower 100x bigger models in next 3 years.&nbsp;</li> <li>Tons of ambiguous problems and you will be tasked with building 0 to 1 solutions for all of them.</li> </ul> <p><strong>What we’re looking for:</strong></p> <ul> <li>BS (or higher) degree in Computer Science, or a related field.</li> <li>10+ years of relevant industry experience in leading the design of large scale &amp; production ML infra systems.</li> <li>Deep knowledge with at least one state-of-art programming language (Java, C++, Python).&nbsp;</li> <li>Deep knowledge with building distributed systems or recommendation infrastructure</li> <li>Hands-on experience with at least one modeling framework (Pytorch or Tensorflow).&nbsp;</li> <li>Hands-on experience with model / hardware accelerator libraries (Cuda, Quantization)</li> <li>Strong communicator and collaborative team player.</li> </ul><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p>At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.</p> <p><em><span style="font-weight: 400;">Information regarding the culture at Pinterest and benefits available for this position can be found <a href="https://www.pinterestcareers.com/pinterest-life/" target="_blank">here</a>.</span></em></p></div><div class="title">US based applicants only</div><div class="pay-range"><span>$135,150</span><span class="divider">&mdash;</span><span>$278,000 USD</span></div></div></div><div class="content-conclusion"><p><strong>Our Commitment to Diversity:</strong></p> <p>Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic under federal, state, or local law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require an accommodation during the job application process, please notify&nbsp;<a href="mailto:accessibility@pinterest.com">accessibility@pinterest.com</a>&nbsp;for support.</p></div>
Senior Staff Machine Learning Enginee...
San Francisco, CA, US; , CA, US
<div class="content-intro"><p><strong>About Pinterest</strong><span style="font-weight: 400;">:&nbsp;&nbsp;</span></p> <p>Millions of people across the world come to Pinterest to find new ideas every day. It’s where they get inspiration, dream about new possibilities and plan for what matters most. Our mission is to help those people find their inspiration and create a life they love.&nbsp;In your role, you’ll be challenged to take on work that upholds this mission and pushes Pinterest forward. You’ll grow as a person and leader in your field, all the while helping&nbsp;Pinners&nbsp;make their lives better in the positive corner of the internet.</p> <p>Creating a life you love also means finding a career that celebrates the unique perspectives and experiences that you bring. As you read through the expectations of the position, consider how your skills and experiences may complement the responsibilities of the role. We encourage you to think through your relevant and transferable skills from prior experiences.</p> <p><em>Our new progressive work model is called PinFlex, a term that’s uniquely Pinterest to describe our flexible approach to living and working. Visit our </em><a href="https://www.pinterestcareers.com/pinflex/" target="_blank"><em><u>PinFlex</u></em></a><em> landing page to learn more.&nbsp;</em></p></div><p>We are looking for a highly motivated and experienced Machine Learning Engineer to join our team and help us shape the future of machine learning at Pinterest. In this role, you will tackle new challenges in machine learning that will have a real impact on the way people discover and interact with the world around them.&nbsp; You will collaborate with a world-class team of research scientists and engineers to develop new machine learning algorithms, systems, and applications that will bring step-function impact to the business metrics (recent publications <a href="https://arxiv.org/abs/2205.04507">1</a>, <a href="https://dl.acm.org/doi/abs/10.1145/3523227.3547394">2</a>, <a href="https://arxiv.org/abs/2306.00248">3</a>).&nbsp; You will also have the opportunity to work on a variety of exciting projects in the following areas:&nbsp;</p> <ul> <li>representation learning</li> <li>recommender systems</li> <li>graph neural network</li> <li>natural language processing (NLP)</li> <li>inclusive AI</li> <li>reinforcement learning</li> <li>user modeling</li> </ul> <p>You will also have the opportunity to mentor junior researchers and collaborate with external researchers on cutting-edge projects.&nbsp;&nbsp;</p> <p><strong>What you'll do:&nbsp;</strong></p> <ul> <li>Lead cutting-edge research in machine learning and collaborate with other engineering teams to adopt the innovations into Pinterest problems</li> <li>Collect, analyze, and synthesize findings from data and build intelligent data-driven model</li> <li>Scope and independently solve moderately complex problems; write clean, efficient, and sustainable code</li> <li>Use machine learning, natural language processing, and graph analysis to solve modeling and ranking problems across growth, discovery, ads and search</li> </ul> <p><strong>What we're looking for:</strong></p> <ul> <li>Mastery of at least one systems languages (Java, C++, Python) or one ML framework (Pytorch, Tensorflow, MLFlow)</li> <li>Experience in research and in solving analytical problems</li> <li>Strong communicator and team player. Being able to find solutions for open-ended problems</li> <li>8+ years working experience in the r&amp;d or engineering teams that build large-scale ML-driven projects</li> <li>3+ years experience leading cross-team engineering efforts that improves user experience in products</li> <li>MS/PhD in Computer Science, ML, NLP, Statistics, Information Sciences or related field</li> </ul> <p><strong>Desired skills:</strong></p> <ul> <li>Strong publication track record and industry experience in shipping machine learning solutions for large-scale challenges&nbsp;</li> <li>Cross-functional collaborator and strong communicator</li> <li>Comfortable solving ambiguous problems and adapting to a dynamic environment</li> </ul> <p>This position is not eligible for relocation assistance.</p> <p>#LI-SA1</p> <p>#LI-REMOTE</p><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p>At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.</p> <p><em><span style="font-weight: 400;">Information regarding the culture at Pinterest and benefits available for this position can be found <a href="https://www.pinterestcareers.com/pinterest-life/" target="_blank">here</a>.</span></em></p></div><div class="title">US based applicants only</div><div class="pay-range"><span>$158,950</span><span class="divider">&mdash;</span><span>$327,000 USD</span></div></div></div><div class="content-conclusion"><p><strong>Our Commitment to Diversity:</strong></p> <p>Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic under federal, state, or local law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require an accommodation during the job application process, please notify&nbsp;<a href="mailto:accessibility@pinterest.com">accessibility@pinterest.com</a>&nbsp;for support.</p></div>
Staff Software Engineer, ML Training
San Francisco, CA, US; , CA, US
<div class="content-intro"><p><strong>About Pinterest</strong><span style="font-weight: 400;">:&nbsp;&nbsp;</span></p> <p>Millions of people across the world come to Pinterest to find new ideas every day. It’s where they get inspiration, dream about new possibilities and plan for what matters most. Our mission is to help those people find their inspiration and create a life they love.&nbsp;In your role, you’ll be challenged to take on work that upholds this mission and pushes Pinterest forward. You’ll grow as a person and leader in your field, all the while helping&nbsp;Pinners&nbsp;make their lives better in the positive corner of the internet.</p> <p>Creating a life you love also means finding a career that celebrates the unique perspectives and experiences that you bring. As you read through the expectations of the position, consider how your skills and experiences may complement the responsibilities of the role. We encourage you to think through your relevant and transferable skills from prior experiences.</p> <p><em>Our new progressive work model is called PinFlex, a term that’s uniquely Pinterest to describe our flexible approach to living and working. Visit our </em><a href="https://www.pinterestcareers.com/pinflex/" target="_blank"><em><u>PinFlex</u></em></a><em> landing page to learn more.&nbsp;</em></p></div><p>The ML Platform team provides foundational tools and infrastructure used by hundreds of ML engineers across Pinterest, including recommendations, ads, visual search, growth/notifications, trust and safety. We aim to ensure that ML systems are healthy (production-grade quality) and fast (for modelers to iterate upon).</p> <p>We are seeking a highly skilled and experienced Staff Software Engineer to join our ML Training Infrastructure team and lead the technical strategy. The ML Training Infrastructure team builds platforms and tools for large-scale training and inference, model lifecycle management, and deployment of models across Pinterest. ML workloads are increasingly large, complex, interdependent and the efficient use of ML accelerators is critical to our success. We work on various efforts related to adoption, efficiency, performance, algorithms, UX and core infrastructure to enable the scheduling of ML workloads.</p> <p>You’ll be part of the ML Platform team in Data Engineering, which aims to ensure healthy and fast ML in all of the 40+ ML use cases across Pinterest.</p> <p><strong>What you’ll do:</strong></p> <ul> <li>Implement cost effective and scalable solutions to allow ML engineers to scale their ML training and inference workloads on compute platforms like Kubernetes.</li> <li>Lead and contribute to key projects; rolling out GPU sharing via MIGs and MPS , intelligent resource management, capacity planning, fault tolerant training.</li> <li>Lead the technical strategy and set the multi-year roadmap for ML Training Infrastructure that includes ML Compute and ML Developer frameworks like PyTorch, Ray and Jupyter.</li> <li>Collaborate with internal clients, ML engineers, and data scientists to address their concerns regarding ML development velocity and enable the successful implementation of customer use cases.</li> <li>Forge strong partnerships with tech leaders in the Data and Infra organizations to develop a comprehensive technical roadmap that spans across multiple teams.</li> <li>Mentor engineers within the team and demonstrate technical leadership.</li> </ul> <p><strong>What we’re looking for:</strong></p> <ul> <li>7+ years of experience in software engineering and machine learning, with a focus on building and maintaining ML infrastructure or Batch Compute infrastructure like YARN/Kubernetes/Mesos.</li> <li>Technical leadership experience, devising multi-quarter technical strategies and driving them to success.</li> <li>Strong understanding of High Performance Computing and/or and parallel computing.</li> <li>Ability to drive cross-team projects; Ability to understand our internal customers (ML practitioners and Data Scientists), their common usage patterns and pain points.</li> <li>Strong experience in Python and/or experience with other programming languages such as C++ and Java.</li> <li>Experience with GPU programming, containerization, orchestration technologies is a plus.</li> <li>Bonus point for experience working with cloud data processing technologies (Apache Spark, Ray, Dask, Flink, etc.) and ML frameworks such as PyTorch.</li> </ul> <p>This position is not eligible for relocation assistance.</p> <p>#LI-REMOTE</p> <p><span data-sheets-value="{&quot;1&quot;:2,&quot;2&quot;:&quot;#LI-AH2&quot;}" data-sheets-userformat="{&quot;2&quot;:14464,&quot;10&quot;:2,&quot;14&quot;:{&quot;1&quot;:2,&quot;2&quot;:0},&quot;15&quot;:&quot;Helvetica Neue&quot;,&quot;16&quot;:12}">#LI-AH2</span></p><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p>At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.</p> <p><em><span style="font-weight: 400;">Information regarding the culture at Pinterest and benefits available for this position can be found <a href="https://www.pinterestcareers.com/pinterest-life/" target="_blank">here</a>.</span></em></p></div><div class="title">US based applicants only</div><div class="pay-range"><span>$135,150</span><span class="divider">&mdash;</span><span>$278,000 USD</span></div></div></div><div class="content-conclusion"><p><strong>Our Commitment to Diversity:</strong></p> <p>Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic under federal, state, or local law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require an accommodation during the job application process, please notify&nbsp;<a href="mailto:accessibility@pinterest.com">accessibility@pinterest.com</a>&nbsp;for support.</p></div>
Distinguished Engineer, Frontend
San Francisco, CA, US; , US
<div class="content-intro"><p><strong>About Pinterest</strong><span style="font-weight: 400;">:&nbsp;&nbsp;</span></p> <p>Millions of people across the world come to Pinterest to find new ideas every day. It’s where they get inspiration, dream about new possibilities and plan for what matters most. Our mission is to help those people find their inspiration and create a life they love.&nbsp;In your role, you’ll be challenged to take on work that upholds this mission and pushes Pinterest forward. You’ll grow as a person and leader in your field, all the while helping&nbsp;Pinners&nbsp;make their lives better in the positive corner of the internet.</p> <p>Creating a life you love also means finding a career that celebrates the unique perspectives and experiences that you bring. As you read through the expectations of the position, consider how your skills and experiences may complement the responsibilities of the role. We encourage you to think through your relevant and transferable skills from prior experiences.</p> <p><em>Our new progressive work model is called PinFlex, a term that’s uniquely Pinterest to describe our flexible approach to living and working. Visit our </em><a href="https://www.pinterestcareers.com/pinflex/" target="_blank"><em><u>PinFlex</u></em></a><em> landing page to learn more.&nbsp;</em></p></div><p>As a Distinguished Engineer at Pinterest, you will play a pivotal role in shaping the technical direction of our platform, driving innovation, and providing leadership to our engineering teams. You'll be at the forefront of developing cutting-edge solutions that impact millions of users.</p> <p><strong>What you’ll do:</strong></p> <ul> <li>Advise executive leadership on highly complex, multi-faceted aspects of the business, with technological and cross-organizational impact.</li> <li>Serve as a technical mentor and role model for engineering teams, fostering a culture of excellence.</li> <li>Develop cutting-edge innovations with global impact on the business and anticipate future technological opportunities.</li> <li>Serve as strategist to translate ideas and innovations into outcomes, influencing and driving objectives across Pinterest.</li> <li>Embed systems and processes that develop and connect teams across Pinterest to harness the diversity of thought, experience, and backgrounds of Pinployees.</li> <li>Integrate velocity within Pinterest; mobilizing the organization by removing obstacles and enabling teams to focus on achieving results for the most important initiatives.</li> </ul> <p>&nbsp;<strong>What we’re looking for:</strong>:</p> <ul> <li>Proven experience as a distinguished engineer, fellow, or similar role in a technology company.</li> <li>Recognized as a pioneer and renowned technical authority within the industry, often globally, requiring comprehensive expertise in leading-edge theories and technologies.</li> <li>Deep technical expertise and thought leadership that helps accelerate adoption of the very best engineering practices, while maintaining knowledge on industry innovations, trends and practices.</li> <li>Ability to effectively communicate with and influence key stakeholders across the company, at all levels of the organization.</li> <li>Experience partnering with cross-functional project teams on initiatives with significant global impact.</li> <li>Outstanding problem-solving and analytical skills.</li> </ul> <p>&nbsp;</p> <p>This position is not eligible for relocation assistance.</p> <p>&nbsp;</p> <p>#LI-REMOTE</p> <p>#LI-NB1</p><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p>At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.</p> <p><em><span style="font-weight: 400;">Information regarding the culture at Pinterest and benefits available for this position can be found <a href="https://www.pinterestcareers.com/pinterest-life/" target="_blank">here</a>.</span></em></p></div><div class="title">US based applicants only</div><div class="pay-range"><span>$242,029</span><span class="divider">&mdash;</span><span>$498,321 USD</span></div></div></div><div class="content-conclusion"><p><strong>Our Commitment to Diversity:</strong></p> <p>Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic under federal, state, or local law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require an accommodation during the job application process, please notify&nbsp;<a href="mailto:accessibility@pinterest.com">accessibility@pinterest.com</a>&nbsp;for support.</p></div>
You may also like