Senior Software Engineer - Machine Learning & Geospatial

We're looking for a Senior Software Engineer to help evolve our Machine Learning capabilities, with a particular focus on closing feature gaps and behavioral differences relative to widely used ML frameworks (e.g., Spark ML, scikit-learn), while continuing to deliver new ML functionality.

This role is ideal for someone who enjoys working across model behavior, system design, and customer expectations - ensuring that ML features behave predictably, perform well at scale, and align with how users expect industry-standard tools to work.

Responsibilities

• 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., * 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.

• 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.

An Exceptional Candidate Will Have

• 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.

Ocient has built a hyperscale enterprise data warehouse platform that enables rapid transformation and analysis of massive (petabyte)-scale data at speeds 10x-50x faster than competitive solutions. Built from the ground up for OLAP-style workloads, Ocient delivers disruptively better price performance at limitless scale.