Impress employers and recruiters.
Choose from hundreds of resume examples.
Impress employers and recruiters.
Choose from hundreds of resume examples.
Tailor your resume to this Research, Finetuning Science role.
Rezi rewrites your resume against Thinking Machines Lab's job description. Free.
Tailor your resume to this Research, Finetuning Science role.
Rezi rewrites your resume against Thinking Machines Lab's job description. Free.
Don't guess if your resume is good enough.
See how it scores against the Research, Finetuning Science posting at Thinking Machines Lab — free, in seconds.
Don't guess if your resume is good enough.
See how it scores against the Research, Finetuning Science posting at Thinking Machines Lab — free, in seconds.
About the Role
Thinking Machines builds tools that enable people to customize AI models for their unique needs, including training model weights. In this role, you will work on frontier customization techniques and help build the post-training engine, Tinker, leveraging a whole-stack understanding of RL science. Your findings will directly influence Tinker's training defaults, API design, and the open-source Tinker Cookbook, while collaborating with internal research teams and contributing to open science for external partners.
Responsibilities
- Advance the science of fine-tuning and frontier post-training techniques.
- Contribute to areas like LoRA and parameter-efficient fine-tuning, focusing on customization quality, efficiency, and reliability.
- Ship research into product by informing Tinker's training defaults and primitives.
- Codify best-practice methods as recipes in the Tinker Cookbook.
- Improve the stability, efficiency, and reliability of large-scale fine-tuning and RL runs on Tinker.
- Share learnings through papers, technical blog posts, and community contributions.
Requirements
- Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.
- Proficiency in Python and familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow, or JAX).
- Comfort debugging distributed training and writing code that scales.
- Clarity in communication and the ability to explain complex technical concepts in writing.
- Strong interest in the mission to enable custom models.
- A strong grasp of probability, statistics, and ML fundamentals.
- Prior experience with RLHF, RLAIF, preference modeling, or reward learning for large models.
- Experience managing or analyzing human data collection campaigns or large-scale annotation workflows.
- Research or engineering contributions in alignment, data-centric AI, or human-AI collaboration.
- Experience with RL training stability techniques for large runs.
- 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
- Deep learning frameworks (PyTorch, TensorFlow, JAX)
- Distributed training
- Communication
- Probability
- Statistics
- Machine Learning fundamentals
- RLHF
- RLAIF
- Preference modeling
- Reward learning for large models
- Human data collection campaigns
- Large-scale annotation workflows
- Alignment
- Data-centric AI
- Human-AI collaboration
- RL training stability techniques
Location
- San Francisco, California
Work Type
- Onsite
Experience Level
- Mid-level
- Senior
Education Level
- Bachelor's degree
- PhD
Salary/Compensations
- $350,000 - $475,000 USD
Benefits
- Health benefits
- Dental benefits
- Vision benefits
- Unlimited PTO
- Paid parental leave
- Relocation support
- Visa sponsorship
About the Company
- The mission of Thinking Machines is to build AI that extends human will and judgment.
- We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication.
- We believe the future worth building is human, and we're hiring people who want to build it.
Equal Opportunity
- 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.