Engineering Manager, Model Routing & Inference
Role summary
Cursor is seeking an Engineering Manager to lead their Model Routing & Inference team. This role involves owning the inference platform that powers all AI interactions, focusing on speed, reliability, and cost-effectiveness at scale. Responsibilities include setting technical direction for cluster management and inference optimization, building systems for dynamic model selection, managing GPU capacity, and designing traffic control mechanisms. The role also requires hiring, growing, and developing a team of strong engineers through coaching and mentorship. The ideal candidate has experience leading teams in high-throughput, low-latency distributed systems, particularly in inference serving or traffic routing, and is adept at making cost/performance tradeoffs at scale.
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
You will lead the Model Routing & Inference team at Cursor, owning the inference platform that powers every AI interaction in the product. This team owns the full inference path: making Cursor's AI faster, more reliable, and more cost-effective at a scale few teams in the world get to operate at. Every agent session, every tab completion, and every chat message flows through your stack.
You'll set technical direction for cluster management, inference optimization, and traffic egress, building the platform that lets the rest of the company move fast without worrying about provider complexity. You'll lead a team of strong engineers, set strong direction for the business, and make the calls that balance latency, cost, reliability, and user experience across millions of daily requests.
What you’ll do
Building and evolving our inference gateway, a single abstraction over every provider's API semantics, so model onboarding becomes a config change.
Building the systems that dynamically select the best model for each request based on cost, latency, and quality.
Managing GPU cluster utilization and capacity planning across providers, optimizing for cost and performance.
Designing routing backpressure and admission control so traffic spikes don't cascade into providers.
Hiring and growing the team: sourcing, interviewing, and closing top inference and systems talent, while developing your engineers through coaching, mentorship, and high-leverage project assignments.
You may be a fit if
You have led engineering teams building high-throughput, low-latency distributed systems, especially in inference serving, traffic routing, or real-time data pipelines.
You're comfortable reasoning about cost/performance tradeoffs at scale (GPU utilization, provider economics, capacity planning) and making decisions with incomplete information.
You have strong software engineering fundamentals and enjoy shipping production systems that handle millions of requests.
Experience with model serving frameworks (vLLM, TensorRT-LLM, TGI), load balancing, or building resilient multi-provider architectures is a plus.
You make good calls in the gray area: weighing reliability, cost, latency, and user experience when there isn't a single "right" answer.
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