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About the Role

This role blends fundamental research and practical engineering, serving as a critical bridge between raw model intelligence and a system that is useful, safe, and collaborative for humans. It is ideal for individuals who enjoy deep theoretical exploration and hands-on experimentation, aiming to shape the foundations of AI learning.

Responsibilities

• Develop and tune post-training recipes, including datasets, training stages, and hyperparameters, measuring the impact of choices on various metrics.
• Iterate on evaluations, optimizing them to ensure they are meaningful and accurately reflect performance improvements.
• Debug and understand training configurations, ensuring functionality and developing deeper insights into AI behavior.
• Scale existing methodologies and develop new ones for post-training, measuring performance metric scaling and exploring new training dataset types.
• Publish and present research to advance the AI community, sharing code, datasets, and insights.

Requirements

• 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 scalable code.
• Clarity in communication and ability to explain complex technical concepts in writing.
• A strong grasp of probability, statistics, and ML fundamentals, with the ability to distinguish real effects from noise and bugs.
• 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.

Skills

• Python
• Deep Learning Frameworks (PyTorch, TensorFlow, JAX)
• Distributed Training
• Scalable Code Development
• Communication
• Probability
• Statistics
• Machine Learning Fundamentals
• RLHF
• RLAIF
• Preference Modeling
• Reward Learning
• Data Collection Campaign Management
• Annotation Workflow Analysis
• Alignment
• Data-Centric AI
• Human-AI Collaboration

Location

• San Francisco, California

Work Type

• Onsite

Experience Level

• Bachelor's degree or equivalent experience
• PhD or equivalent industry research experience

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

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.