Software Engineer, ML Platform

cursor; New York, San Francisco (NY, CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Cursor is seeking a Software Engineer for the ML Platform that powers model training and research insights. You’ll help design and operate core platform systems used by ML researchers and product engineers, collaborating closely with research to turn recurring pain into durable infrastructure.

Join a flat, talented team in a setting that values ownership and shipping quickly on large GPU fleets. Expect to contribute across Telemetry, ML Data Platform, Observability, and DevX & Systems.

Qualifications

• Strong background in systems/infrastructure software engineering focused on building platform primitives.
• Experience owning production distributed systems at scale (ingestion, data pipelines, scheduling).
• Comfort with Linux, cloud and modern orchestration (Kubernetes, Ray or equivalent).

Responsibilities

• Design, build, and operate core platform systems used daily by ML researchers and product engineers.
• Partner 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 and raise the bar.

Skills

Distributed systems

Infrastructure engineering

Linux/Unix

Cloud

Kubernetes

Tools

Terraform

Spark

Ray

OpenTelemetry

Job description

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.