Build systems for realistic reinforcement learning environments at a startup focused on automating coding.
Posted by employer 18 hours ago
First seen on Joblaze 46 minutes ago
Last verified on the company career page 46 minutes ago
Skills & Technologies
What you'll build
- Building platforms for RL environments
- Designing end-to-end factories for data
- Defining quality standards for data
- Creating self-serve APIs and tooling
- Partnering across teams for training tasks
Must have
- strong software engineering fundamentals
- experience with data platforms
- experience with developer tools
- experience with distributed systems
- experience with ML infrastructure
Not disclosed in this posting: compensation, years of experience, visa sponsorship.
Joblaze summary
In the role of Software Engineer on the RL Environments team at Cursor, the individual will focus on developing systems that transform real-world data into high-quality training environments for reinforcement learning. Key skills include strong software engineering fundamentals, experience with data platforms, and the ability to create automated quality checks. This position is well-suited for someone with a background in data engineering or machine learning infrastructure, who thrives in a collaborative environment and enjoys tackling complex, open-ended challenges.
Joblaze insights
- Listed today on Joblaze — tracked directly from Cursor's career page.
- Cursor has 132 other open roles including Software Engineer, Research Tools, Sales Development Representative - ANZ, Deal Desk Analyst - Americas.
- 420 active distributed systems roles on Joblaze right now.
- 133 active reinforcement learning roles on Joblaze right now.
- 87 active developer tools roles on Joblaze right now.
- Typical distributed systems salary we track runs $133,100–$217,331 (median $181,500, across 224 roles). See distributed systems salary trends
Quick facts
- Is the Software Engineer, RL Environments role remote?
- It's hybrid — Cursor expects some on-site time in San Francisco, United States.
- Where is the role based?
- Cursor is hiring for this position in San Francisco, United States.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: Data Platforms, ML infrastructure, developer tools, distributed systems, reinforcement learning.
- What seniority level is this role?
- Cursor targets mid-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Software Engineer, RL Environments role at Cursor.
From the original posting
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 Cursor, 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.