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About the Role
Join a small applied research team working directly with frontier AI research labs on challenging post-training problems. This research-first role focuses on reinforcement learning, training data, and experiments that help improve model capabilities.
Responsibilities
- Work with customer research teams to turn research goals into concrete environments, datasets, experiments, and deliverables.
- Design and run post-training loops using reinforcement learning methods such as GRPO, preference optimization, and supervised fine-tuning.
- Shape and inspect high-fidelity trajectories and training data, with careful attention to correctness and edge cases.
- Build reward functions, rubrics, validators, and evaluation harnesses that resist reward hacking and measure capability gains.
- Analyze post-training signals and experiment results across distributed GPU clusters to guide next steps.
Requirements
- At least 2 years of experience as an applied researcher or research scientist, with hands-on model training experience.
- Practical reinforcement learning and agent experience beyond standard machine learning engineering work.
- Experience at a frontier AI lab, AI research startup, or AI data lab, or a strong research publication or model result on a notable benchmark.
- PhD involving reinforcement learning research is relevant experience.
Skills
- Python
- PyTorch
- FSDP
- FastAPI
- GRPO
- Reinforcement Learning
- Supervised Fine-Tuning
- Distributed GPU clusters
Location
- New York, New York
- Seattle
Work Type
- Hybrid
Experience Level
- 2+ years of experience as an applied researcher or research scientist
Education Level
- PhD
Salary/Compensations
- $300,000 to $500,000
Benefits
- Visa sponsorship is available.