Public track record of impactful technical work with an engaged developer or AI community following.
Recent hands-on experience in software or ML engineering, particularly with strong Python skills.
Proven ability to explain complex technical concepts through documentation or examples.
Working knowledge of LLM training and evaluation, with a willingness to learn new techniques.
Experience managing documentation or tutorial projects from inception to release, incorporating user feedback.
Collaborative experience with engineers or researchers to translate feedback into actionable improvements.
Responsibilities
Own and enhance Model Factory documentation for developers, guiding them from quickstart to advanced workflows.
Create runnable tutorials that illustrate the model improvement cycle, including failure analysis and evaluation design.
Collaborate with Research teams to convert internal workflows into adaptable examples for developers.
Partner with Engineering to publish documentation alongside new features and maintain example code as the platform evolves.
Observe developer interactions with Model Factory to identify confusion points and work with Product and Engineering for resolutions.
Develop technical content in various formats, including hands-on workshops for training workflows.
Support developers using prime-rl and Verifiers, facilitating contributions back to the open-source community.
Assess developer experience through tutorial completion metrics and support inquiries to prioritize improvements.
Benefits
Competitive base salary and equity.
Full medical, dental, and vision coverage for employees and dependents.
Flexible PTO policy.
$200/month health and wellness benefit.
Full visa sponsorship and relocation support.
Professional development budget for courses and conferences.
Quarterly team off-sites, hackathons, and learning opportunities.
Company-covered meals when working from the San Francisco office.
Full Job Description
Role Impact
You'll help developers build with Model Factory by creating documentation and practical tutorials that take them from a first training run to improving a model on their own tasks.
Working directly with our researchers and engineers, you'll turn the workflows we use at Prime into reproducible examples and help developers understand the open-source foundations of the platform, including prime-rl and Verifiers. Responsibilities
Own the Model Factory documentation, giving developers a clear path from their first quickstart to more advanced workflows with reliable API and SDK examples.
Build runnable tutorials that follow a model through the improvement cycle, from investigating failures in traces to designing evaluations and training with SFT or RL, with clear comparisons of results before deployment.
Work with Research and Applied Research to turn internal workflows into reproducible examples that developers can adapt to their own problems.
Partner with Engineering to publish documentation alongside new capabilities and maintain example code as the platform evolves.
Observe developers using Model Factory and work with Product and Engineering to resolve the points of confusion, whether they need a better explanation or a change to the product.
Create technical content in the format best suited to the subject, including hands-on workshops where developers can work through a training workflow with you.
Support developers building on prime-rl and Verifiers, helping them contribute useful environments and examples back to the open-source community.
Use tutorial completion and time to a first successful workflow to assess the quality of the developer experience, drawing on recurring support questions and repeat usage to prioritize improvements.
Requirements
A public track record of useful technical work and an established, engaged following in the developer or AI community, with evidence that your content or open-source projects helped people adopt a tool or build something.
Recent hands-on software or ML engineering experience, with strong Python skills and examples of code you personally built and maintained.
Experience explaining complex technical concepts through documentation or working examples that give developers enough context to reproduce results and understand their limitations.
A working understanding of LLM training and evaluation, with the ability to learn unfamiliar post-training techniques through practical experimentation.
The ability to take a documentation or tutorial project from an initial user problem through to a tested release, incorporating feedback from the people using it.
Experience working closely with engineers or researchers to understand technical details and translate developer feedback into practical improvements.
Useful experience
Hands-on work with SFT or RL, including the design of evaluations and rewards for agent environments.
Contributions to open-source ML infrastructure or developer tools.
Maintaining documentation and example repositories alongside a changing product, including automated checks for broken examples.
Teaching technical audiences through workshops or hands-on community support.
Benefits and Perks
Competitive base salary and equity.
Full medical, dental and vision coverage for employees and dependents.
Flexible PTO.
$200/month health and wellness benefit.
Full visa sponsorship and relocation support.
Professional development budget for courses and conferences.
Quarterly team off-sites, hackathons, conferences and learning opportunities.
Company-covered meals when working from our San Francisco office.
Share links to your technical work and the audience you've built, explaining your personal contribution and the engagement or adoption it generated.
