Senior Software Engineer - Machine Learning

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• We cannot sponsor or transfer any visas, of any kind, at this time*
• The salary offered for this position will be based on a candidate’s experience and skill demonstrated during interviews and other evaluations
• Framework parity and behavioral correctness. Help close feature and semantic gaps against scikit-learn and Spark ML across the model catalog so our models behave the way users coming from those frameworks expect
• New model types and capabilities. Expand the model catalog across classification, regression, time series, and automated model selection, including loss functions and objectives we don't support today.
• Performance at scale. Make training and inference fast on datasets that don't fit anywhere else. Approximate nearest-neighbor acceleration, distributed optimizer tuning, and algorithmic work on the hot paths.
• Numerical and linear-algebra foundations. Help expand the SQL-level linear algebra surface (SVD, eigenvalues, matrix inverse and solve, sparse matrices) and spectral transforms, the substrate under PCS, regression, and optimization.
• Architecture. Our models are compiled into the query plan and execute as a native part of it, rather than running in a separate ML runtime. You'll work inside that architecture and help improve it.
• Collaboration and craft. Write clear design docs, tests, and documentation; investigate issues where behavior diverges from user expectations; and partner with Product, architects, and customer-facing teams to identify gaps before customers hit them.
• 5+ years building production software systems, including solid experience in C++ (or comparable systems-level work in Java/Scala with a willingness to work primarily in C++).
• Hands-on experience implementing or integrating machine learning models in production. You have written the training loop, not just called into a library.
• Working knowledge of numerical methods: gradient-based optimization, loss functions and their gradients, numerical stability, feature scaling, convergence behavior.
• Familiarity with scikit-learn, Spark ML, XGBoost, or comparable frameworks, and awareness of where their defaults and semantics matter.
• Strong instincts around correctness, edge cases, and behavioral consistency – and the discipline to encode them in tests and documentation.
• Ability to work across teams and codebases and turn ambiguous requirements into concrete solutions.
• Experience comparing or validating model behavior across multiple ML frameworks.
• Experience with large-scale data systems, analytical databases, query planners, or distributed execution engines.
• Exposure to optimization (LP/QP/SOCP), spectral methods (FFT/DCT/DWT), causal inference, or probabilistic modeling for the in-database research surface we are building next.
• Experience with automatic differentiation or symbolic gradient generation.
• Familiarity with SQL internals like AST manipulation, expression rewriting, or planner integration.
• Customers see fewer surprises. Our models behave the way someone coming from scikit-learn or Spark ML expects, and where they differ, the difference is intentional and documented.
• Roadmap items such as ARIMA, quantile regression, AutoML, option parity, land with validated correctness against reference implementations.
• Feature gaps are identified from benchmarks and product analysis early, not discovered under customer pressure.
• You deliver across both parity work and broader ML initiatives, balancing short-term needs with long-term quality.