Software Engineer, RL Environments
Data Engineer · Mid · Full Time
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Role
What you'll do.
As a Software Engineer on Cursor's RL Environments team, you'll architect and build the systems that transform real-world data into high-quality reinforcement learning environments at scale. This role sits at the intersection of research, data engineering, and ML infrastructure, requiring you to design end-to-end data pipelines, define quality standards, and create developer-focused APIs that enable internal teams and external contributors to build complex, realistic training environments for AI agents. You'll work with diverse data sources including company data, bot interactions, vendor tasks, and synthetic data while partnering closely with ML Platform and research teams to translate model capability goals into effective training tasks.
Responsibilities
- Build Scalable RL Environment Platforms: Design and implement distributed systems that construct complex, realistic, and diverse reinforcement learning environments capable of supporting end-to-end tasks across multiple knowledge-work domains. Focus on enabling platform scalability, reliability, and performance while maintaining data integrity across heterogeneous sources.
- Engineer End-to-End Data Factory Systems: Design comprehensive data pipelines that ingest massive volumes of raw data from diverse sources (company data, Grok Bot interactions, vendor-built tasks, tutor data, acquired data, and synthetic data) and transform them into production-ready training environments. Establish robust ETL workflows with proper error handling, monitoring, and observability.
- Define and Implement Quality Standards: Establish concrete, automated quality metrics and validation checks across all data sources including vendor, tutor, acquired, and synthetic data. Build automated testing frameworks, data quality dashboards, and continuous validation systems that ensure training data meets model capability requirements.
- Develop Self-Serve Developer Tooling: Create intuitive APIs, SDKs, and command-line tools that accelerate task and environment development for internal engineering teams and external contributors. Design developer experiences that reduce friction in the data creation process while maintaining quality guardrails and consistent standards.
- Cross-Functional Collaboration and Integration: Partner closely with ML Platform teams, research scientists, and model training engineers to translate abstract capability goals into concrete training tasks and environments. Participate in architecture reviews, design discussions, and technical planning sessions to align infrastructure with model training objectives and organizational roadmap.
Qualifications
What we look for.
Technical
Data Platform Architecture and Design
Proven experience designing and building production data platforms, data lakes, or data warehouses. Demonstrated expertise in ETL/ELT pipeline design, data modeling, schema design, and managing data flow at scale across distributed systems.
Distributed Systems Engineering
Strong foundation in designing and implementing distributed systems, including understanding of consistency models, fault tolerance, service orchestration, and horizontal scalability. Experience with orchestration tools, message queues, or workflow engines at scale.
ML Infrastructure and ML Ops
Background in building infrastructure for machine learning workflows, including experience with model training pipelines, feature engineering systems, experiment tracking, model serving, or ML ops tools. Understanding of ML workflows and how data quality impacts model training.
Software Engineering Fundamentals
Solid computer science foundation with expertise in software design patterns, system design, code quality practices, testing strategies, and performance optimization. Ability to write clean, maintainable code and make pragmatic architectural decisions.
Data Quality and Validation Automation
Experience building automated data quality frameworks, validation systems, and testing pipelines. Ability to translate ambiguous quality requirements into concrete, measurable validation checks and monitoring systems.
Education
Bachelor's Degree in Computer Science or Related Field
Foundational computer science education covering data structures, algorithms, distributed systems, and software engineering principles. Equivalent professional experience with demonstrated mastery of core CS concepts is also valued.
Experience
4-7 Years of Software Engineering Experience
Significant production engineering experience with demonstrated impact building scalable systems. Track record of taking ownership of complex, ambiguous problems and shipping solutions that have been adopted by teams.
Data Platform or Infrastructure Experience
3+ years building or maintaining data platforms, data infrastructure, ML platforms, or developer tools. Experience navigating the complexities of heterogeneous data sources and building systems that handle diverse data formats and qualities.
Cross-Functional Collaboration in Technical Roles
Experience working effectively across product, research, and engineering teams. Demonstrated ability to translate between technical and non-technical stakeholders, understand business requirements, and drive projects to completion in matrix organizations.
Skills
Required
Backend Systems Design
Ability to architect scalable, maintainable backend systems with proper separation of concerns, error handling, and observability. Experience designing APIs that are intuitive and robust.
Data Pipeline Development
Proficiency building production data pipelines that handle data ingestion, transformation, validation, and output. Understanding of batch processing, streaming architectures, and hybrid approaches.
Database and Query Optimization
Strong knowledge of SQL, relational databases, and query optimization. Ability to design efficient schemas, write performant queries, and understand indexing strategies for large datasets.
Python or Equivalent Backend Language
Production-level proficiency in Python, Java, Go, or similar systems programming languages. Ability to write performant, maintainable code and leverage language-specific tools for data processing.
Testing and Quality Assurance
Deep experience with unit testing, integration testing, and end-to-end testing frameworks. Ability to design testable systems and establish quality gates in production pipelines.
Problem Decomposition and Ambiguity Navigation
Strong ability to break down ill-defined problems into concrete work items, define success metrics, and drive projects from conception to production with minimal guidance.
Preferred
Machine Learning Fundamentals
Nice to haveUnderstanding of ML workflows, model training processes, reinforcement learning concepts, and how data characteristics impact model behavior. Experience working closely with ML teams or working on ML infrastructure.
Reinforcement Learning Environments
Nice to haveFamiliarity with RL frameworks like OpenAI Gym, DeepMind environments, or building custom task environments. Understanding of how RL training data differs from supervised learning and the importance of environment design.
Data Quality and Observability Tools
Nice to haveExperience with data quality frameworks (Great Expectations, Monte Carlo Data, etc.), data profiling tools, and comprehensive monitoring/alerting systems for data pipelines.
API Design and Developer Experience
Nice to haveTrack record designing intuitive developer APIs, building SDKs, or creating developer tools. Understanding of what makes APIs discoverable, easy to use, and well-documented for internal and external users.
Infrastructure-as-Code and Automation
Nice to haveExperience with infrastructure automation, containerization (Docker/Kubernetes), configuration management, and provisioning tools. Comfort building scalable, reproducible systems.
Tech stack
Languages
Frameworks
Databases
Tools
Other
Compensation
Pay and benefits.
Base·USD 180,000 – 280,000
Equity·Stock options
Benefits
Equity Compensation
Ownership stake in Cursor through stock options, directly aligning your success with company growth as an AI-focused developer tools company experiencing rapid adoption.
Health and Wellness Coverage
Comprehensive medical, dental, and vision insurance plans covering you and your family, plus mental health support and wellness programs.
Professional Development and Learning
Access to educational resources, conference attendance budgets, and dedicated time for skill development in emerging areas like AI/ML infrastructure and distributed systems.
Flexible Work Arrangements
Flexible work location and schedule options that accommodate remote work when needed while supporting in-person collaboration for deep technical work and team building.
Competitive Time Off
Generous paid time off policy, parental leave, and sabbatical options that demonstrate commitment to work-life balance and long-term employee wellbeing.
Collaborative Engineering Culture
Small, talent-dense team environment that values truth-seeking, creative problem-solving, and spirited technical debate. Opportunity to work on mission-critical infrastructure for an AI platform.
Process
Interview steps.
- 01
Initial Screening and Conversation
Hiring team reviews your background, experience with data systems, and understanding of the RL environments problem space. Expect discussion of your past projects, technical decision-making, and collaboration style.
- 02
Technical Interviews (2-3 Sessions)
Focused technical discussions covering system design for data platforms, data pipeline architecture decisions, problem decomposition skills, and your approach to handling ambiguous requirements. May include whiteboarding, design discussions, or reviewing past work samples.
- 03
Onsite Interview and Work Session
Full-day onsite at Cursor's San Francisco office. You'll work on a small real-world project related to RL environment challenges, have discussions with team members about technical and product directions, and meet research and ML platform engineers to assess collaboration fit.
- 04
Team Collaboration Assessment
Throughout the interview process, particular attention is paid to your ability to collaborate across disciplines, translate ambiguous requirements into concrete solutions, and demonstrate ownership of end-to-end problems in complex environments.
Full posting
Original listing.
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.
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