Staff Software Engineer, Agent Quality

New York City · San Francisco · USD 200,000-265,000 / year · 6+ years experience · Posted Sep 24, 2026

At a glance

Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn, X, YouTube, and Instagram.

As a Staff Software Engineer - Agent Quality, you will be a founding member of a new team focused on evaluating and continuously improving Databricks' AI Agents.
You will design and scale the infrastructure, tooling, and developer workflows that let researchers and engineers evaluate agents rigorously.

Stand up the foundational evaluation infrastructure for Genie Agents, enabling rigorous benchmarking, regression detection, and quality measurement across research and product teams.
Build the flywheel that connects evaluation results back into agent improvement — closing the loop between production signals, training, and iterative development.
Shape the long-term technical direction for agent quality infrastructure, with real influence over how Databricks measures and improves its first-party agents and agent development platform.
Help shape the long-term technical direction for agent quality infrastructure as Databricks expands its first-party agents and agent development platform.

6+ years industry experience building software systems
Strong Python programming skills, with experience building production or research infrastructure
Experience building or operating distributed systems, data pipelines, or large-scale infrastructure with a focus on reliability, correctness, and operational maturity
Ability to design pragmatic but rigorous systems that produce trustworthy, reproducible signals for complex applications
Comfort working across ambiguous research and product boundaries, and partnering with both researchers and engineers to turn ideas into robust internal platforms
A high bar for technical quality, strong ownership, and the ability to influence roadmap and execution across multiple teams

Experience with devtools, CI/CD platforms, testing frameworks, observability tooling, or benchmarking infrastructure
Familiarity with how LLM or agent quality is measured — whether through evals, experimentation platforms, or production monitoring