Senior Software Engineer - Machine Learning & Geospatial

Location: Remote, USA

Department: Development - Query Processing and Integrations

Location Type: REMOTE

Employment Type: FULL_TIME

• 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

• Design and implement machine learning features used in production customer workflows.
• Help identify and close feature and behavior gaps between our ML capabilities and common frameworks (e.g., Spark ML, scikit-learn).
• Proactively evaluate semantic differences, defaults, and edge cases that could surprise customers.
• Partner with product, architects, and customer-facing teams to anticipate upcoming customer needs and gaps.
• Investigate and resolve issues where ML behavior diverges from user expectations (e.g., model output, metrics, configuration semantics).
• Contribute to other ML initiatives including new models, metrics, performance improvements, and infrastructure work.
• Analyze and improve the performance of existing ML code, balancing correctness and stability with customer facing latency.
• Write clear design docs, tests, and documentation to make behavior explicit and prevent regressions.

• 5+ years of experience building production software systems.
• Strong proficiency in at least one backend or systems language (e.g., C++, Java, Scala).
• Experience implementing or integrating machine learning models in production.
• Familiarity with ML libraries or frameworks such as Spark ML, scikit-learn, XGBoost, or similar.
• Strong instincts around correctness, edge cases, and behavioral consistency.
• Ability to work across teams and codebases to turn ambiguous requirements into concrete solutions.

• Experience comparing or validating behavior across multiple ML frameworks.
• Experience with large-scale data systems or analytical databases.
• Familiarity with distributed execution, performance tuning, or numerical stability.
• Understanding of spherical geometry and its application to geospatial analytics.

• Customers see fewer surprises when using ML features compared to familiar frameworks.
• ML behavior, defaults, and limitations are well-documented and intentional.
• Feature gaps are identified early, not discovered under customer pressure.
• You deliver across parity work and broader ML initiatives, balancing short-term needs with long-term quality.