Member of Technical Staff, Applied Researcher

ABOUT THE ROLE Join a small applied research team working directly with frontier AI research organizations on challenging post-training problems. This research-first role focuses on reinforcement learning, training data, and experiments that improve model capabilities. WHAT YOU'LL DO - Work with customer research teams to turn open-ended 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 attention to correctness, edge cases, and quality. - 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 determine 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. - Research experience at a frontier AI lab, an AI research or data lab, or through substantial independent work; alternatively, published research or a released model with strong results on a notable benchmark. - Experience with Python, PyTorch, FSDP, FastAPI, GRPO, and distributed GPU clusters. - Experience with post-training methods, training data, reward design, and model evaluation. COMPENSATION & BENEFITS Base salary: $300,000 to $500,000 annually. Visa sponsorship is available. LOCATION Hybrid in New York, New York. Seattle is also listed as a location.