Member of Technical Staff, Applied Researcher

About the Role

Join an applied research team working directly with frontier AI research groups on challenging post-training problems. This research-first role focuses on reinforcement learning, training data quality, and experiments that produce measurable model capability gains.

What You'll Do

• Partner with customer research teams to turn open-ended goals into concrete environments, datasets, experiments, and deliverables.

• Design and run post-training loops, including 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 determine next steps.

What We're Looking For

• At least 2 years of experience as an applied researcher or research scientist, with experience in a research role.

• Hands-on model training experience in a frontier AI lab, AI research startup, or AI data organization, or a strong research publication or model release with notable benchmark results.

• Reinforcement learning and agent experience beyond standard machine learning engineering work.

• Strong practical experience with Python, PyTorch, FSDP, FastAPI, GRPO, and distributed GPU clusters.

• A PhD involving reinforcement learning research or experience at a leading frontier AI lab is relevant.

Compensation & Benefits

Base salary range: $300,000 to $500,000 annually. Visa sponsorship is available.

Location

Hybrid, based in New York, New York. Seattle is also listed as a work location.