Responsibilities
- Set technical strategy and multi-quarter roadmap for ML platform.
- Own cross-team architecture, RFCs, and design reviews.
- Build core components: ML lifecycle (MLflow), training pipelines, Kubernetes model serving, batch inference, observability, and developer experience.
- Evaluate inference frameworks, resolve performance bottlenecks, establish standards.
- Partner with Data Science and Engineering teams; mentor engineers.
Requirements
- 15+ years software engineering with production systems ownership.
- 4+ years end-to-end ML systems in production (on-call, architecture).
- Strong Kubernetes experience (deployments, autoscaling, observability).
- Hands-on with MLflow or equivalent.
- Expertise in inference tradeoffs, reliability (SLOs), Principal-level leadership.
- Experience with managed ML platforms (Databricks, SageMaker, Vertex AI).
Nice-to-Haves
- Databricks experience.
- Inference/serving frameworks.
- Feature stores, optimization systems, LLM workflows.
Compensation
Base salary: $212,000 - $287,000 USD (midpoint $250,000), plus equity and benefits.