Research Pre-Training Science

Thinking Machines Lab's mission is to empower humanity through advancing collaborative general intelligence. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals.

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.

The role of pre-training researchers sits at the core of our roadmap. This work advances the science of how large models learn from data. You’ll explore new pre-training methods, architectures, and learning objectives that make model training efficient, robust, and aligned with human goals.

This role blends fundamental research and practical engineering, as we do not distinguish between the two roles internally. You will be expected to write high-performance code and read technical reports. It’s an excellent fit for someone who enjoys both deep theoretical exploration and hands-on experimentation, and who wants to shape the foundations of how AI learns.

• Research and develop new methodologies for pre-training.
• Work in areas such as scaling, architecture, algorithms, or optimization of large scale training runs depending on your research interest and experience.
• Design data curricula and sampling strategies that improve learning dynamics and model generalization.
• Collaborate with infrastructure and data teams to conduct large-scale experiments efficiently and reproducibly.
• Publish and present research that moves the entire community forward. Share code, datasets, and insights that accelerate progress across industry and academia.

• Ability to design, run, and analyze experiments thoughtfully, with demonstrated research judgment and empirical rigor.
• Experience with distributed or high-performance computing environments.
• Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, Tensor Flow, or JAX). Comfortable with debugging distributed training and writing code that scales.
• Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.
• Clarity in communication, an ability to explain complex technical concepts in writing.

• A strong grasp of probability, statistics, and ML fundamentals. You can look at experimental data and distinguish between real effects, noise, and bugs.
• Prior experience training or analyzing large-scale models, or contributing to pre-training or foundation model research.
  
  Strong publication record or open-source contributions in representation learning, optimization, scaling laws, or other areas of pre-training.
• Familiarity with curriculum learning, data selection, or active learning techniques.
• Experience designing or maintaining evaluation frameworks for large models.
• Contributions to open datasets, research publications, or data tooling.
• PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience.

• 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 $350,000 - $475,000 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.

As set forth in Thinking Machines' Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.

Thinking Machines Lab will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the California Fair Chance Act, the San Francisco Fair Chance Ordinance, and any other applicable state or local fair chance ordinance or law.