Post-Training Research Scientist for LLM Data & Evaluation

Key Talent Solutions; San Francisco (CA); On-site; USD 150,000 - 250,000; Full time

Job summary

Key Talent Solutions seeks a Research Scientist - Post Training to advance how training data shapes LLM capabilities, generalisation and alignment. This hands-on role blends research, evaluation and collaboration with external partners.

You will design SFT and RL experiments, build reproducible benchmarks, publish results, and translate findings into practical improvements for data quality and production. A founding-level background from a top technical university is preferred.

Qualifications

• Must have held a founding-level role
• Graduated from a top-tier technical university
• Strong familiarity with LLM training and evaluation methodologies
• Ability to design lightweight experiments and extract actionable insights
• Experience at a frontier AI research lab

Responsibilities

• Design and run controlled SFT and RL experiments to measure the impact of training datasets on model performance
• Evaluate how different data sources affect model capabilities, generalisation and alignment
• Help develop public evaluations, benchmarks and new data types that advance LLM capabilities
• Publish external-facing research, technical reports and blog posts
• Collaborate with internal stakeholders to improve data quality based on experimental findings
• Analyse complex or noisy experimental results and turn them into clear, actionable insights
• Contribute to reproducible evaluation methods across models, capabilities and technical domains

Skills

LLM training

Evaluation methodologies

Experiment design

Cross-domain collaboration

Hands-on builder

Frontier AI experience

Education

Top-tier technical university

Job description

Key Talent Solutions seeks a Research Scientist - Post Training to advance how training data shapes LLM capabilities, generalisation and alignment. This hands-on role blends research, evaluation and collaboration with external partners.

You will design SFT and RL experiments, build reproducible benchmarks, publish results, and translate findings into practical improvements for data quality and production. A founding-level background from a top technical university is preferred.