Staff Software Engineer- Foundation Model Inference

The shape of it

In the posting’s own words

As part of the AI team, you'll build the platforms and products that power everything from data apps, AI agents, model training, model serving, and Vector Search. You'll be joining a high-agency, high-visibility team operating at the frontier of AI infrastructure — with deep ties to research, product, and real-world enterprise use cases. Databricks Mosaic AI is one of our fastest-growing businesses, helping thousands of our customers democratize AI within their organizations. We're building the products and infrastructure that power the next generation of AI.

What it asks for · 4

• 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

Also a plus

• Exposure to platforms like 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

What the job covers

• Build LLM infrastructure powering large-scale inference workloads for customers through partner models (OpenAI, Anthropic, Gemini) and self-hosted models (Qwen, GPT-OSS, Llama)
• 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

Tools and skills named

Words the posting leans on

• infrastructure9×
• model8×
• data6×
• platform6×
• customers5×
• build4×
• experience4×
• enterprise3×
• products3×
• workloads3×
• apis2×
• building2×
• deep2×
• developer2×
• distributed2×
• engineers2×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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