Build LLM infrastructure powering large-scale inference workloads for customers through partner models and self-hosted models.
Improve reliability, latency, and efficiency of distributed AI workloads.
Collaborate with platform, infra, and ML teams to deliver seamless end-to-end experiences.
Shape how developers and data scientists build and interact with AI on Databricks.
What we look for
8+ years of experience in backend or infrastructure engineering.
Experience with distributed systems, scalable APIs, or cloud-native infrastructure.
Experience with real-time serving, ML infrastructure, or GPU orchestration.
Familiarity with service-oriented architecture, deployment pipelines, and system observability.
Bonus points for exposure to SageMaker, Vertex AI, or Azure ML; contributions to OSS projects like MLflow, PyTorch, Ray, vLLM, SGLang; built developer platforms or internal tools supporting AI workflows.