Staff Machine Learning Engineer

• Build and operate ML systems end to end: data and feature pipelines, training and evaluation, batch or online inference, deployment, monitoring, retraining, failure handling, and iteration after launch.
• Shape ML infrastructure strategy as a key decision-maker: evaluate build-versus-buy options, choose platform components, and set the roadmap for how the team's production footprint grows.
• Productionize LLM-based products: evaluation harnesses, guardrails, cost and latency management, and the operating patterns that keep them reliable after launch.
• Lead the technical direction and hands-on delivery of one or two prioritized ML initiatives at a time.
• Translate business opportunities into clear technical approaches, success measures, and plans for adoption and impact evaluation.
• Establish reusable standards for testing, reproducibility, versioning, observability, documentation, and responsible model operation; review designs and code across the team.
• Partner with Product, Engineering, Data Engineering, Revenue Management, and other business owners to make technical tradeoffs and integrate models into products and operating workflows.
• Mentor engineers and data scientists, raise the team's engineering judgment, and create leverage beyond your own projects while remaining hands-on.
• What makes you a great fit
• Typically 8+ years building and operating production ML or data-intensive systems, with several of those years owning systems that business decisions depended on.
• Evidence of Staff-level scope: you have led ML systems from idea through sustained production use, set technical direction beyond one project, and created leverage for other engineers and data scientists.
• Expert Python and strong software engineering fundamentals: testing, code review, CI/CD, packaging, dependency management, and observability. Strong SQL and comfort working with large datasets in a cloud warehouse.
• Experience designing and running production ML systems: training pipelines, batch or online inference, deployment, monitoring, retraining, incident response, and iteration after launch.
• Hands-on experience with cloud infrastructure (AWS or equivalent), containers and orchestration (Kubernetes or equivalent), and the tradeoffs involved in choosing and operating ML platform components.
• Enough modeling and evaluation judgment to be a real partner to data scientists: offline and online evaluation, leakage, drift, and basic experiment design.
• Ability to connect technical decisions to business outcomes and communicate tradeoffs clearly to technical, product, business, and executive audiences.
• A self-directed, collaborative working style: you can prioritize across a broad problem space, receive and give direct feedback, influence without formal authority, and remain hands-on in a lean, remote team.
• Helpful experience
• Snowflake, dbt, feature stores, model registries, workflow orchestration, and practical MLOps or model-governance patterns.
• LLM evaluation, retrieval-augmented generation, agents, or cost and latency optimization for LLM products in production.
• Demand forecasting, dynamic pricing, revenue management, price-elasticity estimation, causal inference, or optimization.
• Marketplace, travel, hospitality, or another domain where models influence high-frequency operating decisions.
• Standing up ML infrastructure or platform patterns at a company that did not have them yet.
• An advanced degree in a quantitative field, or equivalent depth demonstrated through applied work.

None flagged.