Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

As a Sr. Software Engineer, you'll enhance Databricks' customer support with AI-driven systems, ensuring improved automation and user experience across support engineering operations.

As a Staff Backend Software Engineer on the AI Platform team, you will build infrastructure for AI applications, improve distributed AI workloads, and collaborate with various teams to enhance developer and data scientist experiences.

As a Staff Backend Software Engineer, you will design and implement systems for high-scalability and low-latency inference using AI models, driving architectural decisions and mentoring teams.

The Staff Backend Software Engineer will design and implement systems for Databricks Model Serving, focusing on scalability, reliability, and operational excellence in serving AI/ML models.

The role involves deep troubleshooting, root cause analysis, and architectural optimization in the Data and AI ecosystem to enhance platform reliability and supportability.

As a Senior Engineer at Databricks, you'll design scalable systems for AI/ML model serving, improve performance, and lead technical initiatives.

The Head of Corporate Engineering manages cloud infrastructure, security, and engineering tools while leading a high-performing team. Responsibilities encompass securing enterprise environments, enabling collaboration, and ensuring compliance with industry standards.

Sell Databricks Lakebase to retail accounts by driving activations, running complex multi-stakeholder sales cycles, orchestrating POCs, and aligning Lakebase to measurable business outcomes. Partner with account teams, solution architects, product, marketing, and partners to enable field adoption, compete against legacy databases, and standardize Postgres in strategic accounts.

Lead a research team focused on enhancing large-scale machine learning models' efficiency and performance, developing novel algorithms and collaborating with engineering teams to implement breakthroughs.

Lead a team of researchers to improve large-scale machine learning, focusing on efficiency, optimization, and scaling techniques for AI models.