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About the Role
At Thinking Machines, we build tools that enable people to customize AI models for their unique needs, including training model weights. You will lead the Fine-tuning Science team, setting the research agenda for frontier customization techniques and Tinker, our post-training engine. This player-coach role involves hands-on scientific work, team growth, direction setting, and ensuring findings are integrated into Tinker. You will collaborate with internal research teams, contribute to open science, and engage with external users.
Responsibilities
- Advance the science of fine-tuning and frontier post-training techniques.
- Set the research agenda, choosing problems, placing bets, and owning the roadmap for Tinker's quality, efficiency, and reliability.
- Lead and grow the team by hiring, mentoring, and developing researchers.
- Set the bar for experimental rigor and research taste.
- Stay hands-on in areas like LoRA and parameter-efficient fine-tuning and their interaction with RL and post-training.
- Improve the stability, efficiency, and reliability of large-scale fine-tuning and RL runs on Tinker.
- Represent the work externally 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.
- A track record of leading research – setting direction for a team or a major research effort and delivering on it.
- 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: an ability to explain complex technical concepts in writing and to build alignment across science, systems/infra, product.
- Strong interest in our mission to enable custom models.
- PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience.
- 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.
Skills
- Python
- Deep learning frameworks (PyTorch, TensorFlow, JAX)
- Distributed training
- Scaling code
- Communication
- Probability
- Statistics
- ML 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
- Full-time
Experience Level
- Lead
- Senior
Education Level
- Bachelor's degree or equivalent experience
- PhD or equivalent industry research experience
Salary/Compensations
- $475,000 - $530,000 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.
- 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.