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Machine Learning Engineer
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Selby Jennings
Campbell, CA (In Person)
Full-Time
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Job Description
About the Role I'm currently working with a well‑funded, late‑stage fintech hiring Machine Learning Engineers to solve real, high‑impact problems and deploy models directly into production. MLEs here own models end‑to‑end - from data exploration and feature engineering through deployment, monitoring, and iteration - working on systems that directly influence business and customer outcomes. The team works across domains including credit risk, pricing, growth targeting, and automation, building ML systems that power core decision‑making and replace manual processes. It's a high‑ownership role working on meaningful, real‑world ML problems alongside strong engineers. What You'll Work On As part of a small, highly impactful ML team, you'll contribute across a wide surface area, including: Building and owning production ML models that support core business decisions Developing risk and decisioning models using a mix of third‑party data and first‑party behavioral data Building pricing, targeting, and relevance models that directly drive growth and customer outcomes Creating models that automate manual or operational workflows, reducing reliance on human review Monitoring model performance, diagnosing issues, and detecting distribution shift or data drift Designing model infrastructure and pipelines to support frequent retraining and iteration Participating in system design reviews and code reviews to uphold engineering best practices Partnering closely with Product, Engineering, and Operations to translate business problems into modeling opportunities Developing deep domain expertise to proactively identify new ways data and models can create value What We're Looking For You'll be a strong fit for this role if you: Have experience owning technically complex systems with many moving parts Have built and maintained production‑grade ML models used in real workflows or decision systems Are comfortable operating across multiple disparate systems and data sources Can ramp quickly in new environments with minimal hand‑holding Are comfortable with ambiguity and excited to help define solutions where answers don't yet exist Thrive in highly collaborative environments and enjoy contributing to discussions ranging from system architecture to product strategy Have a deep curiosity about how systems work end‑to‑end and are willing to dive into details to understand them fully Value ownership, accountability, and long‑term model health over short‑term experiments Candidates with backgrounds in large‑scale systems, distributed systems, or other production‑heavy engineering environments tend to do well in this role. Tech Stack You'll work within a modern, production‑oriented stack that includes: