Software Engineer, RL Environments

Cursor
San Francisco, CA
On-site

Who this role is best for

A natural match if you have experience building data platforms for reinforcement learning.

Best fit for

  • Candidates with experience in building data platforms and creating training environments for AI models
    — “building platforms that allows us to build complex, realistic, and diverse RL environments at scale”
  • Engineers who thrive in small, talent-dense teams and are comfortable with open-ended challenges
    — “our team is small and talent dense. We particularly like people who are truth-seeking, passionate, and creative”

Things to consider

  • The role requires in-office presence for the onsite interview and potential project work.
    — “we'll schedule an onsite in our office, where you'll work on a small project”
  • Candidates should be prepared to work across research, platform, and external teams on a regular basis.
    — “Partnering across research, platform, and external teams to translate model-capability goals”

How to stand out

  • Highlight experience with creating repeatable data pipelines from messy, heterogeneous sources.
    — “You’re comfortable building repeatable pipelines from messy, heterogeneous data”
  • Emphasize your ability to define quality standards and automate their enforcement.
    — “You can turn ambiguous quality standards into concrete, automated checks”
  • Showcase projects where you translated high-level goals into concrete training environments or tasks.
    — “translate model-capability goals into effective training tasks and environments”
  • Demonstrate your ability to work in a flat, collaborative team structure with minimal hierarchy.
    — “our organization is very flat, and our team is small and talent dense”
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • high-quality RL environments at scale
  • effective training tasks and environments
Typical background
strong software engineering fundamentalsexperience with ML infrastructure

Skills & requirements

Required

Software EngineeringData PlatformsDistributed SystemsML InfrastructureQuality StandardsAPI Development

Preferred

Model Behavior UnderstandingTask And Environment Development

About the role

Original posting from Cursor via Ashby

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

One of our north stars is a future where teams can hire Grok as a remote colleague. Reaching that future requires training our models in realistic, diverse environments that support complex, end-to-end work across domains.

As a Software Engineer on the RL Environments team at SpaceXAI, you’ll build the systems that turn real-world data into high-quality environments and tasks for reinforcement learning. You’ll create dramatically more realistic training environments while owning the shared platforms and quality layers that help us produce trustworthy data quickly and at scale.

This role sits at the intersection of research, data, and engineering. You’ll work closely with ML Platform and research teams to turn company data, Grok Bot interactions, vendor-built tasks, tutor data, acquired data, and synthetic data into training-ready environments. Your work will make high-quality RL data easier to create, validate, discover, and use across our model-training efforts.

WHAT YOU’LL WORK ON

  • Building platforms that allows us to build complex, realistic, and diverse RL environments at scale, that support end-to-end tasks across knowledge-work domains.
  • Designing end-to-end factory that turns massive raw data into useful and realistic environments.
  • Defining and applying consistent quality standards across vendor, tutor, acquired, and synthetic data.
  • Creating self-serve APIs and tooling that accelerate task and environment development for internal teams and external contributors.
  • Partnering across research, platform, and external teams to translate model-capability goals into effective training tasks and environments.

YOU MAY BE A FIT IF

  • You have strong software engineering fundamentals and experience with data platforms, developer tools, distributed systems, or ML infrastructure.
  • You can turn ambiguous quality standards into concrete, automated checks.
  • You’re comfortable building repeatable pipelines from messy, heterogeneous data.
  • You care about model behavior and can translate capability goals into tasks and experiments.
  • You collaborate well across disciplines and own open-ended problems end to end.

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.

Source: Cursor careers (Ashby)

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