Staff Software Engineer- Foundation Model Inference

P-1930

At Databricks, we are passionate about enabling data and AI teams to solve the world's toughest problems - from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business.

Founded by engineers - and customer-obsessed - we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started.

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.

The Foundation Model Inference team is the backbone of Databricks' generative AI capabilities. We build the infrastructure that enables our customers to serve, scale, and optimize frontier models with enterprise-grade reliability and performance. Our Foundation Model APIs provide a unified platform that gives customers access to LLMs with the governance, flexibility, and scalability required for enterprise production workloads.

We are looking for high-agency engineers who are excited to work on powering model inference at enterprise scale.

The impact you will have

• 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

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 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

Pay Range Transparency

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles.

Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range.

The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.

Local Pay Range $190,000—$265,000 USD

Benefits

At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region

Compliance

If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.