Member of Technical Staff, Applied Researcher

About the Role

Join a small applied research team working with frontier AI 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 research teams to translate open-ended goals into concrete environments, datasets, experiments, and deliverables.

• Design and run post-training workflows, including 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 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, AI research startup, or AI data lab, or evidence of impactful research through a publication or a model performing well on a notable benchmark.

• Strong knowledge of reinforcement learning and training data, supported by research or applied work.

• 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 range is $300,000 to $500,000 annually. Visa sponsorship is available.

Location

Hybrid in New York, New York, with an additional location in Seattle, Washington.