Senior Software Engineer - Machine Learning & Geospatial
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AI Summary
Design and implement production machine learning features while closing behavioral gaps between internal capabilities and industry-standard frameworks. Collaborate with cross-functional teams to ensure ML features are predictable, performant, and align with customer expectations.
Job Title: Senior Software Engineer - Machine Learning & Geospatial
Location: 100% Remote (US Based Only)
- We cannot sponsor or transfer any visas, of any kind, at this time*
Hiring Manager: Senior Engineering Manager
Estimated salary range: $165,000 to $190,000
- The salary offered for this position will be based on a candidate’s experience and skill demonstrated during interviews and other evaluations
Job 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.
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