Software Engineer, Agent Evaluation and Quality

About this role

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not.

• Function: Engineering
• Seniority: mid

Location & pay

On-site · San Francisco · CA · United States No pay range was listed on the public board feed.

Source

Jobrow reads the public Ashby board for Cursor. First seen 2 months ago; last seen 10 days ago; closed 9 days ago. We are not the employer — apply only on the company ATS.

Posting

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars. SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next. ABOUT THE ROLE As a Software Engineer on the Agent Quality team at SpaceXAI, you’ll build the measurement, evaluation, and feedback-loop infrastructure that makes the Cursor core agent reliably better over time. This role sits at the intersection of product, data, and engineering: you’ll instrument what matters, help define how we judge quality, build pipelines and tooling to analyze agent behavior at scale, and partner closely with research, product, and infrastructure teams to turn insights into improvements. Your impact will compound across every Cursor product built on the shared harness—and across high-stakes decisions around model choice, quality, and cost. WHAT YOU’LL WORK ON - Designing and building best-in-class AI evaluation system: curated datasets, offline replay, scorers / judges, regression alerts, and dashboards. - Designing feedback loops from real usage: collecting, cleaning, and interpreting user signals to inform model and harness changes. - Developing analysis tooling and workflows for debugging agent behavior: deep dives on failure modes, clustering themes, and surfacing actionable insights. - Improving reliability and guardrails by making quality measurable and operational: defining “good/bad/degraded” sessions, alerting, and triage primitives. YOU MAY BE A FIT IF - You’ve built and operated evaluation or measurement systems, such as AI evals, experimentation, ranking/relevance, or search quality. You can turn ambiguous “quality” questions into concrete metrics, pipelines, and decisions. - You have strong data acumen, and can collaborate effectively with data scientists and researchers. - You have taste and strong opini