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Model Engineer

Cursor is hiring a Model Engineer in Palo Alto, United States. It pays $160k-$250k a year and Level rates it ; you can apply on Cursor.

Level's read on this job

  • On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
  • Tools and skills the posting cites include Grok, PyTorch and JAX.
  • Level classifies it as a senior role.
  • Level lists 113 other open roles at Cursor.
  • The role was posted in the last few days.

AI in this role

Drive reinforcement learning and mid-training research to scale frontier coding and computer-use AI agents.

GrokPyTorchJAXPythonRustC
reinforcement-learningmachine-learning-researchdistributed-trainingdata-quality

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars.

SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.

Model Engineer

SpaceXSI is building Super Intelligence to help humanity understand the universe. We train frontier coding agents and scale RL on real user data to make them increasingly effective. We're looking for Research Scientists who can drive effective RL or mid-training research in a small-team setting. You'll own ambiguous, hard research problems end-to-end: forming hypotheses, designing experiments, building the training/eval/data needed to test them, and pushing results into the next model.

RESPONSIBILITIES:

  • Design and run rigorous experiments that turn research ideas into measurable gains in model capability

  • Improve data recipes through data quality, deduplication, and mixture experiments

  • Define what makes RL data and rewards good, including grader design and preventing reward hacking

  • Advance training science, from architecture and scaling experiments to post-training recipes for coding and computer-use agents

  • Build evals and targeted tasks that expose model weaknesses and guide where the team invests next

  • Partner with engineers to take promising results from small experiments to frontier-scale training runs

BASIC QUALIFICATIONS:

  • Experience writing production-quality code in Python, PyTorch, JAX, Rust, or C++

PREFERRED SKILLS AND EXPERIENCE:

  • Able to handle ambiguous research tasks with little guidance, and to own a research direction end to end

  • Care an unusual amount about data quality, and can dive into the data when appropriate

  • Truth seeking, aiming to learn more about the science than to prove your ideas are correct

  • Experience with RL algorithms for language models, scaling laws, or building RL environments and evals

  • Experience with distributed training at scale and debugging problems that only show up at scale

  • Experience training frontier-scale models

  • Strong background in RL and machine learning fundamentals

  • An excellent programmer and software engineer, comfortable working across the research stack in Python and PyTorch or JAX

  • Hands-on experience training large models, through pretraining, post-training or RL

  • A track record of original work in ML, such as new methods or real improvements, shown through research, open source or production impact

  • MS or PhD in CS, ML, math, physics or a related field, or equivalent research experience

ADDITIONAL REQUIREMENTS:

  • Onsite role: must be able to work in person at the Palo Alto office

COMPENSATION AND BENEFITS:

The annual compensation range for this role is listed below.

Annual Salary:

$160,000 - $250,000 USD

Your actual level and base salary will be determined on a case-by-case basis and may vary based on the following considerations: job-related knowledge and skills, education, and experience.

Base salary is just one part of our total rewards package at SpaceXSI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks.

How we rate this

Model Engineer at Cursor rates 100 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Reinforcement learningMachine Learning ResearchDistributed TrainingData QualityGrokPyTorchJAXPython

Questions you could be asked

  1. Tell me about a project where reinforcement learning was part of your work. What did you do?
  2. Tell me about a project where machine learning research was part of your work. What did you do?
  3. Tell me about a project where distributed training was part of your work. What did you do?
  4. Tell me about a project where data quality was part of your work. What did you do?
  5. Walk me through how you've used Grok in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: Reinforcement learning, Machine Learning Research, Distributed Training, Data Quality, and Grok. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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