Research Scientist / Engineer, AI Training Data
Overview
This role drives the design and implementation of verifiable training data for advanced AI systems at a Y Combinator-backed startup. The work spans LLMs, robotics, and AI for science, integrating physics simulators, formal proof systems, and executable tests. You'll collaborate directly with founders to shape technical direction from day one, with a focus on publications and benchmarks. This position offers a unique chance to define research culture and data infrastructure from the ground up.
What You'll Do6
- 1Design and develop methods for generating verifiable training data grounded in physics simulators, scientific databases, formal proof systems, and executable tests.
- 2Research and implement techniques that project laws of physics, biological facts, and self-consistent logic into natural language data.
- 3Build scalable pipelines and tooling for data generation, validation, and quality assurance using Python and PyTorch.
- 4Collaborate on research agendas targeting frontier AI models in LLMs, robotics, and scientific AI.
- 5Drive the creation of benchmarks and publications that advance the field of AI training data.
- 6Debug and optimize data generation workflows to improve model training outcomes.
Requirements6
- 10-12 years of experience in machine learning, deep learning, NLP, robotics, or AI for science.
- 2Research Scientists: proven track record of publishing or producing impactful research in transformers or RLHF.
- 3Research Engineers: strong software engineering skills with experience building ML pipelines or research infrastructure at scale.
- 4Familiarity with LLM fine-tuning, RLHF, or model evaluation.
- 5Experience with formal methods, theorem proving, symbolic AI, physics simulation, or scientific computing is a strong plus.
- 6Ability to work on-site in San Francisco, California.
Salary Insight
Salary not disclosed in listing
Location
Required Skills
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