Quick overview
Job description
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.
What You'll Do
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.
What We're Looking For
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.
Strong working knowledge of Python, PyTorch, FSDP, FastAPI, GRPO, and distributed GPU clusters.
PhD involving reinforcement learning research is relevant experience.
Compensation & Benefits
Base salary is $300,000 to $500,000 annually. Visa sponsorship is available.
Location
Hybrid role based primarily in New York, New York, with Seattle also listed as a location.