Senior ML Engineer – In-Database & Remote

Blue Bear Capital; United States; On-site; USD 165,000 - 190,000; Full time

Job summary

Ocient is hiring a Senior Software Engineer to advance in-database machine learning. You will implement new model types, fill gaps against scikit-learn and Spark ML, and optimize training and inference at scale within the database engine.

You will work on a small, hands-on team, owning meaningful roadmap pieces end-to-end and collaborating with Product and engineering to deliver robust ML capabilities by 2026.

Qualifications

• 5+ years building production software systems, including solid experience in C++ (or comparable systems-level work).
• Hands-on experience implementing or integrating machine learning models in production.
• Working knowledge of numerical methods: gradient-based optimization, loss functions, numerical stability.
• Familiarity with scikit-learn, Spark ML, XGBoost, or comparable frameworks, and awareness of defaults/semantics.
• Strong instincts around correctness and edge cases, with testing/docs discipline.
• Ability to work across teams and turn ambiguous requirements into concrete solutions.

Responsibilities

• Implement new ML model types and close feature gaps against existing frameworks.
• Make ML training and inference fast at scale on large datasets.
• Expand the SQL-level linear algebra surface and related spectral transforms.
• Contribute to architecture by integrating models into query plans as native parts.
• Write clear design docs, tests, and documentation; collaborate with Product and customer teams.

Skills

C++ experience

Production ML

Numerical methods

Familiar with sklearn/spark/xgboost

Testing & documentation

Cross-team collaboration

Job description

Ocient is hiring a Senior Software Engineer to advance in-database machine learning. You will implement new model types, fill gaps against scikit-learn and Spark ML, and optimize training and inference at scale within the database engine.

You will work on a small, hands-on team, owning meaningful roadmap pieces end-to-end and collaborating with Product and engineering to deliver robust ML capabilities by 2026.