ML Platform Engineer
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Requirements
• BS in Computer Science or a relevant technical field. • 3+ years of professional software development experience with a proven track record of shipping high-quality applications and services. • Experience working collaboratively with machine learning engineers, data scientists, or applied scientists on large-scale software projects involving machine learning models. • Deep expertise in library design, API design, data structures, and algorithms. • Strong familiarity with Python. • Python • Tech Stack: Our backend is pure Python (NumPy, Pandas, Torch, PySpark, Cython, orchestrated in Airflow). We use Databricks as our data warehouse. While we'd like you to have very good familiarity with Python, many of our problems are stack-agnostic. • Tech Stack:
Responsibilities
• In your first 3 months, you might deliver a feature that helps generalize model configuration, enables no-code model deploys for our various ML solutions, or vastly improves integration testing across our ML systems. • In your first 3 months • By the end of your first 6 months, you will have owned the implementation of significant scalability improvements and additions to our ML platform. This might include new feature pipelines that power our recommendation engine, or work to stand up the first instance of real-time inference at Afresh. • By the end of your first 6 months