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About the Role

This role advances the science of how large models learn from data by exploring new pre-training methods, architectures, and learning objectives. It blends fundamental research and practical engineering, requiring high-performance coding and deep theoretical exploration.

Responsibilities

• Research and develop new methodologies for pre-training.
• Work in areas such as scaling, architecture, algorithms, or optimization of large scale training runs.
• 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.

Requirements

• 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.
• A strong grasp of probability, statistics, and ML fundamentals.
• 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.

Skills

• Python
• PyTorch
• TensorFlow
• JAX
• Deep Learning
• Distributed Training
• High-Performance Computing
• Probability
• Statistics
• Machine Learning Fundamentals
• Representation Learning
• Optimization
• Scaling Laws
• Curriculum Learning
• Data Selection
• Active Learning
• Evaluation Frameworks

Location

• San Francisco, California

Work Type

• Full-time

Experience Level

• Entry-level to Senior

Education Level

• Bachelor's degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline
• PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline; or, equivalent industry research experience

Salary/Compensations

• $350,000 - $475,000 USD

Benefits

• Generous health, dental, and vision benefits
• Unlimited PTO
• Paid parental leave
• Relocation support

About the Company

• The mission of Thinking Machines is to build AI that extends human will and judgment.

Equal Opportunity

• 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.