- 5+ years of Python engineering. You build production services, not scripts, and you have opinions about testing them.
- Real experience running ML in production — serving, retraining, monitoring, and the specific ways ML systems fail that ordinary services don't.
- Solid software architecture fundamentals: scalable APIs, microservices, data flow design.
- Cloud platform depth – we run on GCP and Databricks; AWS or Azure background transfers fine. Plus Git and CI/CD as a matter of course.
- Working English, written and spoken.
Plus real depth in at least one of
- Kubernetes, CI/CD and infrastructure-as-code — the platform side.
- Distributed data processing: Spark on Databricks, Dataproc, or equivalent.
- Feature stores, model registries, or experiment tracking tooling — MLflow, Unity Catalog, Vertex AI Model Registry.
- Evaluation and experiment design – you know why a metric moved, and when it didn't move for the reason everyone assumes.
Also good: you use agentic coding tools daily and have a view on where they help and where they quietly don't. We build agents for a living, and people who use them tend to have better instincts about them.
The pay range actually offered will take into account a variety of potential factors considered in compensation, including but not limited to skills, qualifications, geographic location, accomplishments, experience, credentials, internal equity and business needs, and may vary from the range listed above.