Research, Pre-Training Science

Thinking Machines Lab Inc.; San Francisco, Northern (CA, KY); Hybrid; USD 350,000 - 475,000; Full time

Benefits offered by this job

Health, dental, vision benefits

Unlimited PTO

Paid parental leave

Relocation support

Job summary

Thinking Machines Lab Inc. is seeking a pre-training researcher to advance how large models learn from data. You will write high-performance code and engage in both theoretical and practical work to shape foundations of AI learning.

The role blends research with hands-on engineering, with emphasis on scalable experiments, data curricula, and collaboration across teams. Visa sponsorship is available for qualified candidates.

Qualifications

• Ability to design, run, and analyze experiments with empirical rigor.
• Experience with distributed or high-performance computing environments.
• Proficiency in Python and at least one DL framework (PyTorch, TensorFlow, or JAX).
• Bachelor’s degree or equivalent in CS/ML/Physics/Math or related field with strong grounding.
• Clear communication of complex ideas in writing.

Responsibilities

• Research and develop new pre-training methodologies.
• Explore scaling, architecture, algorithms, or optimization for large-scale training.
• Design data curricula and sampling strategies to improve learning dynamics.
• Collaborate with infrastructure and data teams for large-scale experiments.
• Publish and present research to advance the field, sharing code and datasets.

Skills

Experiment design

Distributed computing

Python

Deep learning frameworks

Theoretical grounding

Education

PhD in CS/ML/Physics/Math

Bachelor equivalent

Tools

PyTorch

TensorFlow

JAX

Job description

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

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.

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.

• 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.

Logistics

• 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.