Member of Technical Staff, Applied Researcher

About the Role

Join an applied research team working directly with frontier AI labs on challenging post-training problems. You will help translate open-ended research goals into experiments and deliverables, with a focus on reinforcement learning, training data, and measurable model improvements.

What You'll Do

• Partner with research teams to turn ambiguous 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, focusing on correctness, edge cases, and quality.

• Build reward functions, rubrics, validators, and evaluation harnesses that resist reward hacking and measure capability gains.

• Analyze training signals and experimental 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 2 to 10 or more years of overall relevant experience.

• Hands-on model training experience in a frontier AI lab, AI research startup, or data lab, or demonstrated research results through a publication or model performing well on a notable benchmark.

• Practical reinforcement learning and agent experience beyond standard machine learning engineering work.

• Strong understanding of reinforcement learning and training data, with evidence of hands-on research results.

• Experience with Python, PyTorch, FSDP, FastAPI, GRPO, and distributed GPU clusters.

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

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 work location.