Careers at Cursor

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.

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 the RL Data team, you’ll design and build the tools that researchers and external contributors use to create, review, submit, and monitor the environments and tasks behind Cursor’s reinforcement-learning runs. This is a full-stack product-engineering role embedded in a research team. You’ll own the review and acceptance experience end to end: from rollout and transcript inspection, task-quality signals grader and reward-hacking analysis, to the workflows that move a submission into training. From there, you’ll build authoring interfaces that let researchers, vendors, and domain experts create and improve environments and tasks quickly and confidently. Your work will significantly shorten the loop from a task idea or data sources, to candidate task, to trusted training data. What you’ll work on Create fast, trustworthy workflows for vendors and research team to interact effectively with each other — vendor task creation and iteration, vendor submissions, and task acceptance into training. Build review tools for inspecting and comparing rollouts, transcripts, grader outputs, and other signals of task quality. Develop environment-health, failure-search, versioning, and catalog experiences that make training data easy to understand, manage, and extend. Establish a shared component kit, then use it to build self-serve interfaces for creating and improving tasks with quality checks inline. You may be a fit if You’ve shipped full-stack products and owned systems from user interface through storage or services, using technologies such as TypeScript and React alongside Node, Python, or Go. You’ve built dense, data-facing tools such as transcript viewers, diffing systems, review queues, observability products, or operational dashboards—and you have strong opinions about how structured data should be rendered. You’ve built or maintained a design system or component library and can establish durable product and engineering conventions for a fast-moving team. You’ve designed review, QA, moderation, fraud, or acceptance workflows where users had an incentive to get past the checks, and you know how to keep those systems honest. You care about data quality, and are willing to inspect raw data. Experience with evaluations, graders, reinforcement learning, or data-quality systems is helpful but not required. You move quickly under ambiguity, collaborate closely with researchers and domain experts, and take open-ended problems from rough need to reliable product. Applying If there appears to be a fit, we’ll schedule two or three short technical interviews focused on frontend craft for dense data and system design for a review-and-acceptance workflow. After that, we’ll invite you onsite to work on a small project using real rollouts, discuss ideas, and meet the team. #LI-DNI

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. Software Engineer, Reinforcement learning SpaceXAI is building the future of coding. We train frontier coding agents and scale RL on real user data to make them increasingly effective. About the role As a Software Engineer on the RL Data team at SpaceXAI, you'll create the tasks, rewards, and environments that train our coding agents. The team owns the data that goes into training: what the model is asked to do, how we score it, and the setups it learns in. What you’ll do Designing a task set that teaches a specific agent capability, then iterating on it from traces and evals until the model actually gets better. Reading a pile of agent traces, finding a failure mode or a surprising behavior, and building a system that surfaces more of the same. Turning a one-off recipe into something other teams can reuse: better rewards, cleaner environments, tighter data quality. Partnering with research on whether a dataset is actually teaching the thing we think it is. You may be a fit if You write careful, fast code and have strong software engineering fundamentals. You like setting tasks: breaking a fuzzy capability into something concrete you can measure. You have an infra, data, or distributed systems background. RL experience is a plus, not a requirement. You enjoy looking at messy real-world agent behavior and turning it into a dataset or a tool.

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 We're hiring new grad engineers to join SpaceXAI software engineering teams. You'll own real product work on the surfaces our users live in every day: Grok Bot, Cloud Agents, Cursor and the systems underneath. You will design, build, and ship delightful and bulletproof products and systems that engineers love to use every day. This role values a strong attention to details, first principles reasoning, and great taste. That means picking up concrete problems, driving them end to end (UI through a bit of backend when needed), and raising the bar on taste and correctness while you move fast. You'll work alongside a small, talent-dense team with high ownership and little hierarchy. Strong fundamentals matter. So does judgment about when to use AI tools as leverage without giving up correctness. You may be a fit if: You're graduating in Spring 2027 (CS, software engineering, computer engineering, or a related technical field). You've already built something great — an app, OSS, or shipped product we can open and review ( required ). You blend strong engineering and AI tools as leverage without giving up correctness. You learn fast, take ownership, and have already done something meaningfully hard early (internships or equivalent industry experience welcome). Sample projects include: Adding a small UI improvement so AI-generated code or PRs are easier to read and review Turning a teammate’s rough idea into a working prototype over a few days, then iterating based on feedback Shipping a contained feature (frontend + a bit of backend), then fixing bugs and making it feel smoother after people use it 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. In your application, please share an example of an exceptional technical project or product feature you've built that best demonstrates your technical capabilities (GitHub, live app, OSS, or shipped product).

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 We're hiring new grad engineers to join SpaceXAI software engineering teams. You'll own real product work on the surfaces our users live in every day: Grok Bot, Cloud Agents, Cursor and the systems underneath. You will design, build, and ship delightful and bulletproof products and systems that engineers love to use every day. This role values a strong attention to details, first principles reasoning, and great taste. That means picking up concrete problems, driving them end to end (UI through a bit of backend when needed), and raising the bar on taste and correctness while you move fast. You'll work alongside a small, talent-dense team with high ownership and little hierarchy. Strong fundamentals matter. So does judgment about when to use AI tools as leverage without giving up correctness. You may be a fit if: You're graduating in Spring 2027 (CS, software engineering, computer engineering, or a related technical field). You've already built something great — an app, OSS, or shipped product we can open and review ( required ). You blend strong engineering and AI tools as leverage without giving up correctness. You learn fast, take ownership, and have already done something meaningfully hard early (internships or equivalent industry experience welcome). Sample projects include: Adding a small UI improvement so AI-generated code or PRs are easier to read and review Turning a teammate’s rough idea into a working prototype over a few days, then iterating based on feedback Shipping a contained feature (frontend + a bit of backend), then fixing bugs and making it feel smoother after people use it 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. In your application, please share an example of an exceptional technical project or product feature you've built that best demonstrates your technical capabilities (GitHub, live app, OSS, or shipped product).

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 SpaceXAI, 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.

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 SpaceXAI, 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.