Engineering Manager, Evals
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- onsite
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- FullTime
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1 with evidence8 unclear or unstated- Remote policy
- onsite
“Provider workplace type: onsite”
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- FullTime
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Original job description
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Our mission is to automate coding. The first step in our journey is to build the best tool for professional programmers, using a combination of inventive research, design, and engineering. Our organization is very flat, and our team is small and talent dense. We particularly like people who are truth-seeking, passionate, and creative. We enjoy spirited debate, crazy ideas, and shipping code.
About the Role
As an Engineering Manager on the Evals team at SpaceXAI, you’ll lead the group responsible for creating high-signal evaluation datasets for coding agents and building the tools engineers use to write and run them. The team also owns online evaluation systems that track agent quality in production, and the close integration between online and offline evaluations.
The evaluation systems that this team builds, including CursorBench, are critical in the development of our coding models and the quality of our Cursor agents. Your impact will compound across every Cursor product and every Cursor model by making quality measurable, comparable, and easy to improve.
What you’ll do
Set the eval roadmap end-to-end—what we measure, why it matters, and how signals turn into shipping + training decisions.
Lead and grow a high-impact team of engineers and researchers building eval datasets and developer-friendly tools to write and run evals.
Guide the next generation of CursorBench so it continues to reflect real developer workflows at SpaceXAI, and expand it with new evals that measure other properties developers value.
Define crisp online quality signals and turn regressions into robust guardrails.
Integrate evals into decision-making cadence for launches, deploys, and model training loops.
You may be a fit if
You’ve led engineering teams shipping production systems and have strong people leadership and coaching skills.
You can align research, product, data, and infrastructure on what “good” means—and turn that into durable metrics, processes, and release/training rituals.
You have good taste and strong opinions on model and agent behaviors, and you stay up-to-date on emerging research and industry trends.
You have strong data acumen, and can collaborate effectively with data scientists and researchers.
You’ve built and operated evaluation or measurement systems (e.g., AI evals, experimentation platforms, ranking/relevance, search quality, or reliability instrumentation).
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