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. About the Role 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. Note: This is an "evergreen role" that we keep open on an on-going basis to express interest in this research area. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role. What You’ll Do 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. Skills and Qualifications Minimum qualifications: 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, TensorFlow, 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. Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but at least some: 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