Back-End ML Engineer

Industry: Biotechnology
Company size: 1-10

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Job description

A next-generation computational platform that is committed to radically improving treatments and saving lives is looking for a Back-End ML Engineer. As an engineer, you will work on several impactful and interesting projects, including finding and transforming new datasets, enhancing the efficiency of neural nets, and productionalizing deep learning workflows. The company is on a mission to solve drug delivery with its deep learning platform playing a critical role. This is an exciting opportunity to join a collaborative, mission-driven team to solve challenging problems through first principles.


Job Responsibilities:

  • Design and build production data pipelines for ingesting consumptions in a big data architecture
  • Work with a variety of AWS tools like Spark (PySpark), Glue, Hudi, Kinesis, DMS, EMR, and Lambdas for processing data
  • Develop a Data lake on S3 with landing, raw, trusted, and curated zones
  • Configure AWS Redshift/Redshift Spectrum Data LakeHouse
  • Work on automating processes and infrastructure using Python CDK

Job Requirements:

  • Bachelor’s/Master’s degree in Engineering, Computer Science (or equivalent experience)
  • At least 3+ years of relevant experience as a back-end engineer
  • Experience in working with Python is required
  • Prior experience of working in a high-growth startup is ideal
  • Knowledge of SDLC principles (Unit Testing, Git, Jira, etc.) will be helpful
  • Must possess the expertise of scripting complex database queries in SQL
  • Understanding of Postgres and other relational databases is required
  • Experience working with cloud computing platforms and some of their services (AWS EC2, S3, Athena, Kinesis, Lambda, Kafka and/or Google Cloud Compute Engine, Cloud Storage, BigQuery, BigTable, etc.)
  • Must be willing to collaborate and possess excellent written and verbal communication skills
  • Experience working with machine learning tools such as Tensorflow, PyTorch, Scikit-learn, Pandas, etc., is preferred
  • Track record of working on Spark will be beneficial
  • Ability to work on GPUs/TPUs will be an added advantage

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