Machine Learning EngineerApply
Who You Are
You’re a data hound. You’re comfortable pouring over billions of dynamic data points and bending them to your will. You’ve leveraged the Apache stack to build persistent machine learning algorithms and you’ve played inside Apache Hive data warehouses. You know what it takes to set up Apache “big data” stacks and apply persistent machine learning models using Spark single-handedly.
As our first Machine Learning Engineer, you will own, along with our Data Engineers, the design, implementation, and optimization of our core data and data modeling products. You’ll help us apply artificial intelligence to our market leading real estate agent matching algorithm, build our data warehouse infrastructure, and pioneer innovative business cases for using big data in real estate.
What You’ll Do:
- Create predictive models for real estate transactions using a dynamic dataset
- Refine our agent matching algorithm to improve our core product
- Design, model and build a large-scale data warehouse, using ETL and other related technologies.
- Work closely with data scientists to optimize and productize machine learning models.
- Work closely with engineering to implement and scale your machine learning models
- Build infrastructure and systems for tracking data quality, usage, and consistency.
- Design and develop new data products
- Lead innovative ideas for solving challenges in real estate domain
- Be the thought-leader for how we could use data and predictive algorithms to improve user experience and revenue potential from the AI products
- B.S. or M.S.. in Computer Science or a related field
- 3-5 years of experience in developing a data pipeline with custom ETL that accommodates data from multiple sources of data in multiple formats
- Familiarity with at least one scripting language: R, Python, or Scala.
- Experience using SQL to query databases.
- Expertise in end to end big data architecture including the ability to design pipelines, design machine learning environment and work with data scientists to create persistent machine learning models.
- Expertise in Hadoop ecosystem products and frameworks such as HDFS, Hbase, Sqoop/Flume/Kafka, Kudu.
- Model production in Spark using MLlib
- Application deployment using AWS, Virtual Machines, or Docker
- Minimum 2 Years of Data engineering experience in Hadoop environment.