Member of Technical Staff, Applied Researcher

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

Visa sponsorship is available.

Location

Hybrid role based primarily in New York, New York, with Seattle also listed as a location.