ML Engineer

Key Highlights

• Own ML models across their full lifecycle – from data pipelines and feature engineering to training, evaluation, deployment and monitoring; choosing the right metrics and guarding against leakage, overfitting and drift.
• Run and improve our ML platform – own the team’s GitOps CI/CD and release process, monitor serving endpoints, latency and load in Datadog, and define the SLOs and alerting that keep models reliable in production.
• Turn ML into business value across the org – collaborate with risk, operational and product teams to spot and ship ML opportunities, from credit scoring to AI for operational intelligence, sharing your expertise through code reviews and tech watch.
• You have 5+ years building and shipping ML in production: strong Python and SQL, solid ML fundamentals (evaluation, leakage, over/under-fitting), and clean, tested, reviewable code. We expect you to be familiar with some elements of our stack.
• You’re hybrid – hands-on with MLOps and infrastructure (data pipelines, monitoring, system design, live prediction / streaming) and at ease reasoning about latency, scale and reliability.