Skip to content
Thinking Machines LabThinking Machines LabSan Francisco, CA

Research, Pre-Training Science

Conducts research on pre-training methodologies for large AI models, develops new architectures and data strategies, runs large-scale experiments, and publishes findings. Requires strong ML fundamentals, Python proficiency, and experience with deep learning frameworks.

350k – 475k
On-siteAI Research

About the 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:

  • 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

Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.

Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

Skills

PythonPyTorchTensorFlowJAXDistributed TrainingHigh-Performance ComputingMachine LearningDeep LearningScaling LawsOptimizationCurriculum LearningData SelectionActive Learning

Similar roles

AI Research jobs
Thinking Machines Lab

Research, Audio Expertise

Thinking Machines LabSan Francisco, CA

Conducts research to advance audio capabilities in AI models, designing and training large-scale multimodal systems, building audio data pipelines, and publishing findings. Requires ML expertise, Python proficiency, and experience with deep learning frameworks.

350k – 475k
On-siteAI Research
Anthropic

Research Engineer / Scientist, Alignment Science

AnthropicSan Francisco, CA

Conducts experimental ML research on AI alignment and safety for powerful systems, focusing on scalable oversight, control, and stress-testing. Requires strong software/ML engineering, empirical research experience, and Python proficiency.

350k – 500k
HybridAI Research
Anthropic

Research Engineer, Production Model Post Training

AnthropicSan Francisco, CA +2

Research Engineer implements and scales post-training techniques like Constitutional AI and RLHF for production AI models, optimizing capabilities, alignment, and safety. Requires strong Python skills, ML systems experience, and ability to handle complex distributed training pipelines.

350k – 500k
HybridAI Research
Anthropic

Research Scientist, Interpretability

AnthropicSan Francisco, CA

Conducts mechanistic interpretability research to reverse-engineer language models, developing methods to understand neural network algorithms for AI safety. Requires scientific research background, Python proficiency, and collaborative engineering mindset.

350k – 850k
HybridAI Research
Anthropic

Research Engineer, Pretraining Scaling

AnthropicSan Francisco, CA

Research Engineer optimizes and scales production pretraining of frontier AI models, handling performance, debugging, experiments, and on-call incidents. Requires expertise in JAX, TPU, PyTorch, or large-scale ML systems with a 50/50 research-engineering balance.

350k – 850k
On-siteAI Research