Member of Technical Staff, Applied Researcher

About the Role

Join a small applied research team at an AI data infrastructure company, working directly with advanced AI research organizations on challenging post-training problems. This research-first role focuses on using reinforcement learning and high-quality training data to improve model capabilities.

What You'll Do

• Work with customer research teams to turn open-ended research 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.

• Evaluate training signals to identify which approaches produce meaningful model improvements.

• 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 designed to resist reward hacking and measure capability gains.

• Analyze experiment results and post-training signals 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 reinforcement learning and model training experience.

• Experience at a frontier AI lab, newer AI research organization, or AI data lab, or a strong research publication or model release demonstrating results on a notable benchmark.

• Deep understanding of reinforcement learning and training data; this is not a general machine learning engineering role without RL experience.

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

• Research experience with post-training, preference optimization, supervised fine-tuning, or related methods.

Compensation & Benefits

Annual salary range: $300,000 to $500,000 USD. Visa sponsorship is available.

Location

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