Software Engineer, ML Platform

Neura Market; San Francisco, Northern (CA, KY); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Cursor is seeking a Software Engineer for the ML Platform to build infrastructure that turns real product usage into better models. You will join four teams and work on core platform systems that researchers and engineers rely on daily.

You will collaborate with ML researchers and product engineers, own reliability and performance, and ship iteratively in a flat, high-ownership environment. This role is in-person across our SF/NYC offices.

Qualifications

• Strong background in systems / infrastructure software engineering.
• Experience with production distributed systems at scale.
• Familiarity with Linux, cloud, and orchestration tools.
• Willingness to work closely with ML researchers and product engineers.

Responsibilities

• 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, raise the bar.

Skills

Distributed systems

Infrastructure engineering

Linux

Cloud platforms

Kubernetes

Ray

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.