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About the Role

This role blends research with large-scale data engineering to help assemble pre-training datasets and data systems for next-generation AI models. You will design and implement methods for sourcing, curating, and analyzing pre-training data for quality and performance, working with automated pipelines and human-in-the-loop processes. This position is ideal for individuals passionate about the intersection of data, machine learning, and systems, and who are excited by the challenge of shaping frontier AI.

Responsibilities

Design and implement techniques for curating, sourcing, and filtering large-scale text, code, and multimodal data.
Develop data quality metrics and analysis to measure coverage, diversity, and representativeness across sources.
Collaborate with research and infrastructure teams to scale data processing systems efficiently and reproducibly.
Investigate and mitigate data risks, including privacy, safety, and licensing concerns, to ensure responsible and ethical data use.
Continuously evaluate dataset improvements by analyzing their downstream effects on model learning and behavior.
Publish and present research that moves the entire community forward.
Share code, datasets, and insights that accelerate progress across industry and academia.

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 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.
A strong grasp of probability, statistics, and ML fundamentals.
Experience with curation, preprocessing, and analysis of large-scale text, code, or multimodal datasets.
Prior experience in data engineering, dataset construction, or large-scale web data processing for machine learning models.
Experience evaluating or improving training data quality and knowledge of data ethics, safety, and licensing frameworks relevant to AI dataset creation.
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.

Skills

Python
Deep learning frameworks (PyTorch, TensorFlow, JAX)
Debugging distributed training
Writing scalable code
Communication
Probability
Statistics
Machine Learning fundamentals
Data curation
Data preprocessing
Data analysis
Data engineering
Dataset construction
Large-scale web data processing
Data quality evaluation
Data ethics
Data safety
Licensing frameworks for AI dataset creation
Open datasets
Research publications
Data tooling

Location

San Francisco, California

Work Type

Full-time

Experience Level

Entry-level to Senior

Education Level

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

Equal Opportunity

We sponsor visas.