Software Engineer, ML Platform

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 a Software Engineer on ML Platform at Cursor, you'll build the infrastructure that turns real product usage into better models — and keeps research moving fast on large GPU fleets. ML Platform is organized into four teams. Depending on your background, you may join any of them:

• Telemetry — Own the collection and serving path that turns real product use into a record research can trust; without slowing the product, and under a small, explicit policy. Client-side or high-volume ingestion experience is a plus.
• ML Data Platform — Build the shared environments and pipeline substrate researchers extend, so new experiments don’t fork their own stack.
• Observability — Make it easy for researchers to start, watch, and debug their own runs.
• ML DevX and Systems — Shorten the path from idea to a trusted run on the research fleet.

We're looking for strong distributed-systems and infrastructure engineers who want to sit next to research and ship platform primitives that move the product.

We're in-person with cozy offices in North Beach, San Francisco, Palo Alto, and Manhattan, New York, complete with well-stocked libraries. What you’ll do

• Design, build, and operate core platform systems used daily by ML researchers and product engineers
• Partner closely with research to turn recurring pain into durable infrastructure
• Own reliability, performance, and developer experience for the systems in your lane
• Ship iteratively in a flat, high-ownership environment. Measure impact, then raise the bar

You may be a fit if

• You have a strong background in systems / infrastructure software engineering and enjoy building platforms other engineers depend on
• You've owned production distributed systems at meaningful scale (ingestion, data pipelines, scheduling/orchestration, or similar)
• You're comfortable across Linux, cloud and/or bare metal, and modern orchestration (Kubernetes, Ray, or equivalent)
• You like working closely with ML researchers and product engineers
• You thrive where ownership is high and the feedback loop is short

Especially strong backgrounds by team

• Telemetry: event ingestion, product analytics pipelines, OpenTelemetry / tracing, reliable data APIs
• Product Data Platform: data frameworks, Spark / Flink / Ray, ML dataset and training-data infrastructure
• Observability: experiment / run monitoring, debug and eval tooling, agent-friendly observability UX
• ML DevX and Systems: GPU / cluster scheduling, job queues, node health, research compute developer experience

Applying

If there appears to be a fit, we'll reach out to schedule 2-3 short technicals. After, we'll schedule an onsite in our office, where you'll work on a small project, discuss ideas, and meet the team.

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.

As a Software Engineer On ML Platform At Cursor, You'll Build The Infrastructure That Turns Real Product Usage Into Better Models — And Keeps Research Moving Fast On Large GPU Fleets. ML Platform Is Organized Into Four Teams. Depending On Your Background, You May Join Any Of Them

• Telemetry — Own the collection and serving path that turns real product use into a record research can trust; without slowing the product, and under a small, explicit policy. Client-side or high-volume ingestion experience is a plus.
• ML Data Platform — Build the shared environments and pipeline substrate researchers extend, so new experiments don’t fork their own stack.
• Observability — Make it easy for researchers to start, watch, and debug their own runs.
• ML DevX and Systems — Shorten the path from idea to a trusted run on the research fleet.

We're looking for strong distributed-systems and infrastructure engineers who want to sit next to research and ship platform primitives that move the product.

We're in-person with cozy offices in North Beach, San Francisco, Palo Alto, and Manhattan, New York, complete with well-stocked libraries. What you’ll do

• Design, build, and operate core platform systems used daily by ML researchers and product engineers
• Partner closely with research to turn recurring pain into durable infrastructure
• Own reliability, performance, and developer experience for the systems in your lane
• Ship iteratively in a flat, high-ownership environment. Measure impact, then raise the bar

You may be a fit if

• You have a strong background in systems / infrastructure software engineering and enjoy building platforms other engineers depend on
• You've owned production distributed systems at meaningful scale (ingestion, data pipelines, scheduling/orchestration, or similar)
• You're comfortable across Linux, cloud and/or bare metal, and modern orchestration (Kubernetes, Ray, or equivalent)
• You like working closely with ML researchers and product engineers
• You thrive where ownership is high and the feedback loop is short

Especially strong backgrounds by team

• Telemetry: event ingestion, product analytics pipelines, OpenTelemetry / tracing, reliable data APIs
• Product Data Platform: data frameworks, Spark / Flink / Ray, ML dataset and training-data infrastructure
• Observability: experiment / run monitoring, debug and eval tooling, agent-friendly observability UX
• ML DevX and Systems: GPU / cluster scheduling, job queues, node health, research compute developer experience

Applying

If there appears to be a fit, we'll reach out to schedule 2-3 short technicals. After, we'll schedule an onsite in our office, where you'll work on a small project, discuss ideas, and meet the team.