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
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Work with customer research teams to turn research goals into concrete environments, datasets, experiments, and deliverables.
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Design and run post-training loops using reinforcement learning methods such as GRPO, preference optimization, and supervised fine-tuning.
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Shape and inspect high-fidelity trajectories and training data, with careful attention to correctness and edge cases.
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Build reward functions, rubrics, validators, and evaluation harnesses that resist reward hacking and measure capability gains.
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Analyze post-training signals and experiment results across distributed GPU clusters to guide next steps.
What We're Looking For
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At least 2 years of experience as an applied researcher or research scientist, with hands-on model training experience.
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Practical reinforcement learning and agent experience beyond standard machine learning engineering work.
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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.
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Strong working knowledge of Python, PyTorch, FSDP, FastAPI, GRPO, and distributed GPU clusters.
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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.
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