# Software Engineer, RL Environments **Company:** [Cursor](https://scaleengineer.com/companies/cursor) 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. **Role:** Data Engineer **Seniority:** Mid **Locations:** San Francisco **Salary:** 180000–280000 USD [Apply](https://jobs.ashbyhq.com/cursor/c68b3d8f-360d-41f0-84fd-1cb225b19e23) Canonical: https://scaleengineer.com/jobs/cursor/software-engineer-rl-environments --- ## 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. ## Requirements ### education - {"name":"Bachelor's Degree in Computer Science or Related Field","description":"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."} ### technical - {"name":"Data Platform Architecture and Design","description":"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."} - {"name":"Distributed Systems Engineering","description":"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."} - {"name":"ML Infrastructure and ML Ops","description":"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."} - {"name":"Software Engineering Fundamentals","description":"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."} - {"name":"Data Quality and Validation Automation","description":"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."} ### experience - {"name":"4-7 Years of Software Engineering Experience","description":"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."} - {"name":"Data Platform or Infrastructure Experience","description":"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."} - {"name":"Cross-Functional Collaboration in Technical Roles","description":"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 - {"name":"Backend Systems Design","description":"Ability to architect scalable, maintainable backend systems with proper separation of concerns, error handling, and observability. Experience designing APIs that are intuitive and robust."} - {"name":"Data Pipeline Development","description":"Proficiency building production data pipelines that handle data ingestion, transformation, validation, and output. Understanding of batch processing, streaming architectures, and hybrid approaches."} - {"name":"Database and Query Optimization","description":"Strong knowledge of SQL, relational databases, and query optimization. Ability to design efficient schemas, write performant queries, and understand indexing strategies for large datasets."} - {"name":"Python or Equivalent Backend Language","description":"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."} - {"name":"Testing and Quality Assurance","description":"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."} - {"name":"Problem Decomposition and Ambiguity Navigation","description":"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 - {"name":"Machine Learning Fundamentals","description":"Understanding 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."} - {"name":"Reinforcement Learning Environments","description":"Familiarity 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."} - {"name":"Data Quality and Observability Tools","description":"Experience with data quality frameworks (Great Expectations, Monte Carlo Data, etc.), data profiling tools, and comprehensive monitoring/alerting systems for data pipelines."} - {"name":"API Design and Developer Experience","description":"Track 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."} - {"name":"Infrastructure-as-Code and Automation","description":"Experience with infrastructure automation, containerization (Docker/Kubernetes), configuration management, and provisioning tools. Comfort building scalable, reproducible systems."} ## Tech stack ### tools - {"name":"Docker and Container Technologies","description":"Containerization for packaging data processing components, ensuring reproducibility, and enabling reliable deployment of RL environments and task generators across infrastructure."} - {"name":"Git and Version Control","description":"Essential for managing code, environment definitions, and pipeline configurations. Enables collaboration and maintains audit trail of platform changes."} - {"name":"Monitoring and Observability Tools (Datadog, Prometheus, ELK)","description":"Critical for pipeline health monitoring, performance profiling, debugging, and maintaining system visibility in production environments handling diverse data sources."} - {"name":"CI/CD Platforms (GitHub Actions, Jenkins)","description":"Automated testing and deployment pipelines for ensuring code quality, data quality checks, and safe rollout of infrastructure and platform changes."} ### others - {"name":"Reinforcement Learning Frameworks (OpenAI Gym, DeepMind, Stable Baselines3)","description":"Familiarity with standard RL frameworks helps understand how environments interact with training agents and informs environment design decisions."} - {"name":"Graph Databases or Knowledge Graphs","description":"Understanding of graph data structures useful for modeling complex task dependencies, entity relationships, and domain knowledge within RL environments."} - {"name":"Data Serialization Formats (Protobuf, Avro, Parquet)","description":"Knowledge of efficient data formats for handling diverse data sources and ensuring interoperability across platform components and external data providers."} ### databases - {"name":"PostgreSQL","description":"Robust relational database for storing structured environment metadata, task definitions, quality metrics, and system state. Well-suited for OLTP operations and complex queries."} - {"name":"Cloud Data Warehouses (Snowflake/BigQuery)","description":"Scalable analytics infrastructure for processing large volumes of heterogeneous training data, running quality metrics, and enabling data exploration by research and ML teams."} - {"name":"Time-Series Databases","description":"Specialized storage for monitoring metrics, data quality metrics over time, and tracking environment performance characteristics across training runs."} ### languages - {"name":"Python","description":"Core language for data pipeline development, ETL workflows, data transformation, and ML infrastructure at scale. Essential for implementing business logic and data processing workflows."} - {"name":"SQL","description":"Critical for data querying, warehouse interactions, complex analytical queries, and data exploration. Deep SQL knowledge enables efficient data pipeline design and troubleshooting."} - {"name":"TypeScript/JavaScript","description":"Useful for building web-based developer tools, API clients, and tooling that supports the RL environment platform and enables external contributor workflows."} ### frameworks - {"name":"Apache Airflow","description":"Industry-standard workflow orchestration platform for managing complex data pipelines, scheduling, dependency management, and monitoring of ETL processes."} - {"name":"Kubernetes","description":"Container orchestration for managing distributed data processing workloads, scaling compute resources, and managing microservices that comprise the RL environment platform."} - {"name":"Apache Spark or Pandas","description":"Large-scale data processing frameworks for transforming and aggregating data from diverse sources. Spark for distributed processing at scale, Pandas for interactive data exploration and prototyping."} - {"name":"FastAPI or Django","description":"Web frameworks for building self-serve APIs and developer tools that expose RL environment functionality and task creation capabilities to internal and external users."} ## Benefits ### benefits - {"name":"Equity Compensation","description":"Ownership stake in Cursor through stock options, directly aligning your success with company growth as an AI-focused developer tools company experiencing rapid adoption."} - {"name":"Health and Wellness Coverage","description":"Comprehensive medical, dental, and vision insurance plans covering you and your family, plus mental health support and wellness programs."} - {"name":"Professional Development and Learning","description":"Access to educational resources, conference attendance budgets, and dedicated time for skill development in emerging areas like AI/ML infrastructure and distributed systems."} - {"name":"Flexible Work Arrangements","description":"Flexible work location and schedule options that accommodate remote work when needed while supporting in-person collaboration for deep technical work and team building."} - {"name":"Competitive Time Off","description":"Generous paid time off policy, parental leave, and sabbatical options that demonstrate commitment to work-life balance and long-term employee wellbeing."} - {"name":"Collaborative Engineering Culture","description":"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."} ## Compensation - **max:** 280000 - **min:** 180000 - **currency:** USD - **stockOptions:** true ## Interview process ### steps - {"name":"Initial Screening and Conversation","description":"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."} - {"name":"Technical Interviews (2-3 Sessions)","description":"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."} - {"name":"Onsite Interview and Work Session","description":"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."} - {"name":"Team Collaboration Assessment","description":"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 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 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.