True-Talent-Solutions-Partners
United States
New
Data and Evaluation Systems Engineer
- AI
- Machine Learning
- Python
T
AI Summary
Post-training research role measuring how training data affects LLM behavior through controlled SFT and RL experiments. Design and analyze experiments, build evaluation infrastructure and benchmarks, publish findings, and collaborate to improve data quality. Requires 0-4 years in LLM training, post-training, or model evaluation, frontier AI lab experience, independent experiment design, and software engineering skills.
Key Highlights
Controlled SFT and RL experiments on dataset impacts
Evaluation infrastructure and benchmarks
Public evaluations and data types for frontier model research
Research, technical reports, and blog posts
Data quality collaboration with internal partners and research labs
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Key Responsibilities
Design, run, and analyze controlled SFT and RL experiments to assess dataset impacts on model capabilities, generalization, and alignment.
Help build evaluation infrastructure and benchmarks that make results reproducible across models, capabilities, and domains.
Develop public evaluations and data types that support frontier model research.
Publish research, technical reports, and blog posts that communicate findings clearly.
Collaborate with internal partners and research labs to improve data quality based on experimental results.
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Technical Skills Required
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Benefits & Perks
Visa sponsorship
Job Description
About the Role
Join a post-training research team focused on measuring how training data affects model behavior. You will run controlled SFT and reinforcement learning experiments, then turn the results into practical recommendations for improving data quality and model performance.
What You'll Do
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