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.

Requirements

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.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningC++JavaScalaSpark MLscikit-learnXGBoostproduction software systemsperformance improvementscode analysis

Soft Skills

problem-solvingcollaborationcommunicationattention to detailadaptabilitycustomer focusanalytical thinkingproactive evaluationdocumentation skillscross-team collaboration