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About the Role
This role owns the late-stage training responsibility that shapes what our models are fundamentally capable of, including synthetic data strategies, data mix, quality uplift, context extension, and capabilities across coding, math, and reasoning. It blends fundamental research and practical engineering, ideal for someone comfortable working across the boundary of pre-training and post-training.
Responsibilities
- Own the data: Decide what the model needs to see for each capability and area of knowledge, then source, curate, and synthesize it.
- Build pipelines that filter, deduplicate, verify, and rewrite raw material into training-grade datasets.
- Produce data that moves the model and reflects how people really use these models.
- Improve what the model knows: Design and measure interventions that increase knowledge.
- Characterize how knowledge scales with data and when it is retained through post-training.
- Instill behaviors and set the prior: Introduce new behaviors during mid-training and measure their impact on post-training.
- Build the quality pipeline: Own the automatic filtering and scoring stack, train quality classifiers and LLM-based judges, build verifiers for synthetic data.
- Raise the quality bar of the mix significantly.
- Give the team fine-grained control over data attributes and measure their effects.
- Develop and tune mid-training recipes, including datasets, training stages, annealing schedules, and hyperparameters.
- Measure how recipe choices affect metrics, including downstream of post-training.
- Iterate on evals: Define, optimize, and ensure the meaningfulness of evaluations.
- Debug and understand training configuration results.
- Scale and explore: Measure performance scaling with dataset size and explore new training data types.
Requirements
- Proficiency in Python and familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow, or JAX).
- Comfort 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 and ability to explain complex technical concepts in writing.
- A strong grasp of probability, statistics, and ML fundamentals.
- Experience building or owning training datasets for large models.
- Experience with knowledge injection, continued pre-training, or domain adaptation of large models.
- Experience building model-based data quality systems.
- Experience generating synthetic data and understanding its failure modes.
- 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
- Technical writing
- Probability
- Statistics
- Machine learning fundamentals
- Data curation
- Data filtering
- Data mixture design
- Knowledge injection
- Continued pre-training
- Domain adaptation
- Model evaluation
- Data quality systems
- Synthetic data generation
- Alignment
- Data-centric AI
- Human-AI collaboration
Location
- San Francisco, California
Work Type
- Full-time
- Onsite
Experience Level
- Mid-level
Education Level
- Bachelor's degree or equivalent experience
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.
- 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.