The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it.
About TinkerTinker is our fine-tuning API that empowers researchers and developers to customize frontier AI to their needs — opening access to capabilities that have previously been concentrated in a handful of labs. We manage the infrastructure while allowing Tinkerers full flexibility in training open weights models with their own data, algorithms, and for their own needs. We also share the work we’re doing on post-training science broadly – like On-Policy Distillation – via our Connectionism blog.
About the RoleWe’re hiring an engineer focused on developer experience to build Tinker while using Tinker — working hands-on with real users and turning their challenges into product improvements. You’ll write and update cookbook recipes, add library features, prototype integrations, and ensure users can customize models smoothly.
This role is a bridge between Tinker users and our teams. You’ll surface user patterns to guide product and infrastructure priorities, and share what you learn through blog posts, demos, and code examples.
This is a technical-first role with a major user-facing component. It’s ideal for someone who’s who also enjoys talking to creative people. Tinker is flexible by design to empower novel and ambitious use cases for model fine-tuning — your job is to help them succeed.
Write, test, and maintain high-quality code examples, recipes, and documentation in the Tinker cookbook.
Work directly with a variety of users to debug technical issues and optimize their fine-tuning pipelines.
Partner with internal research and infra teams to identify and prioritize improvements to Tinker’s developer experience.
Build tools, integrations, and demos that reduce user friction and make it easy to experiment and deploy models.
Share insights from the field through posts, guides, and talks.
Help develop Tinker’s near-term roadmap and long-term product strategy.
Enabling user success in building on top of an advanced platform is a skill that can be developed through many paths. You might demonstrate that you have it if you:
Experienced with fine-tuning large language models (LLMs) with supervised and reinforcement learning, and how to tune the hyperparameters of these algorithms.
Experienced with developing software libraries, especially open-source libraries.
Are thoughtful about developer experience, with clear ideas around developer ergonomics, onboarding flow, and sharp edges.
Are passionate about engaging with open-source communities, whether by contributing code, answering questions, or sharing learnings.
Have written public or internal code examples, tutorials, or papers that helped others understand and apply technical concepts.
Are comfortable reasoning about model training, fine-tuning, and inference, and can credibly discuss tradeoffs between different approaches.
Location: This role is based in San Francisco, California.
Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $300,000 - $350,000 USD.
Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.
Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
Skills Required
- 5+ years of experience in developer relations, developer experience, or software engineering with significant open source or community-facing work
- Strong software engineering skills and experience writing and shipping production-quality Python code
- Hands-on experience fine-tuning, evaluating, or deploying large language models, including supervised and reinforcement learning
- Ability to tune hyperparameters for supervised and reinforcement learning algorithms
- Ability to reason about model training, fine-tuning, and inference and discuss tradeoffs between approaches
- Deep expertise in post-training LLMs on new domains and improving model capability, including reinforcement learning and agentic training
- Experience contributing to, maintaining, or growing open source software libraries
- Passion for engaging with open source communities
- Thoughtful understanding of developer experience, developer ergonomics, onboarding flows, and usability issues
- Experience writing public or internal code examples, tutorials, cookbooks, papers, or technical content
- Comfort engaging publicly through talks, livestreams, academic research conferences, or meetups
- Familiarity with multimodal text, image, and audio models and their training, fine-tuning, and inference tradeoffs
What We Do
Thinking Machines Lab is an artificial intelligence research and product company. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals. While AI capabilities have advanced dramatically, key gaps remain. The scientific community's understanding of frontier AI systems lags behind rapidly advancing capabilities. Knowledge of how these systems are trained is concentrated within the top research labs, limiting both the public discourse on AI and people's abilities to use AI effectively. And, despite their potential, these systems remain difficult for people to customize to their specific needs and values. To bridge the gaps, we're building Thinking Machines Lab to make AI systems more widely understood, customizable and generally capable. We are scientists, engineers, and builders who've created some of the most widely used AI products, including ChatGPT and Character.ai, open-weights models like Mistral, as well as popular open source projects like PyTorch, OpenAI Gym, Fairseq, and Segment Anything.
.jpeg)
