AI Summary
Seeking a Senior Software Engineer to enhance ML capabilities, focusing on parity with industry frameworks and delivering new functionality. Responsibilities include designing ML features, closing gaps with Spark ML and scikit-learn, and ensuring predictable, scalable performance. Requires 5+ years of production software experience and proficiency in a backend language.
Key Highlights
Evolve Machine Learning capabilities, focusing on closing feature gaps with industry-standard frameworks.
Design and implement ML features for production customer workflows, ensuring predictable behavior and performance at scale.
Partner with cross-functional teams to anticipate customer needs and resolve ML behavior discrepancies.
Key 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.
Technical Skills Required
Benefits & Perks
$165,000 to $190,000 annual salary
100% Remote (US Based Only)
Nice to 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.
Job Description
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*
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
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.
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- 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.
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- 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.
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