Member of Technical Staff, Applied Researcher

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.