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-focused role helps translate open-ended research goals into experiments and training data that improve model capabilities.

What You'll Do

• Partner 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.

• Evaluate which training signals improve model performance and shape high-fidelity trajectories and datasets with careful attention to correctness and edge cases.

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

• Analyze post-training signals and experiment results across distributed GPU clusters to guide subsequent research.

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, emerging AI lab, or AI data lab, or a strong research publication or model result on a notable benchmark.

• Strong understanding of reinforcement learning and training data, with demonstrated research outcomes.

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

Compensation & Benefits

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

Location

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