Senior Software Engineer - Machine Learning & Geospatial

View all jobsSenior Software Engineer - Machine Learning & Geospatial Remote, USADevelopment - Query Processing and IntegrationsRemoteFull-timeAbout Ocient:Ocient is building OcientAIQ™ – a complete ecosystem for delivering trusted agentic AI solutions at petabyte scale, for the organizations that can't afford to get AI wrong. Our customers protect networks, secure nations, and power the global economy. The problems we solve are genuinely hard, and the work matters. Founded in 2016 by the team that built Cleversafe (acquired by IBM in 2015), Ocient is headquartered in Chicago with a remote-first global team. We are a carbon-neutral company backed by leading investors including Greycroft, OCA Ventures, In-Q-Tel, and Buoyant Ventures.Do not contact Ocient directly to apply for a role. For security purposes, any applications received via email will be deleted.Job Title: Senior Software Engineer - Machine Learning & GeospatialLocation: 100% Remote (US Based Only)We cannot sponsor or transfer any visas, of any kind, at this time*Hiring Manager: Senior Engineering ManagerEstimated salary range: $165,000 to $190,000The salary offered for this position will be based on a candidate’s experience and skill demonstrated during interviews and other evaluationsJob Description: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.Ideal Qualifications: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.What success looks like: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.Technical Assessment Notice: Candidates will...