True Talent Solutions Partners
Member of Technical Staff, Applied Researcher
Applied researcher on a small team doing post-training work for frontier AI research organizations: designing RL training loops (GRPO, preference optimization, SFT), building reward functions and evaluation harnesses, and analyzing experiments across distributed GPU clusters. Hybrid role in New York or Seattle; Python/PyTorch stack.
- Compensation
- $300K–$500K / yr
- Location
- New York
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Full time
- Experience
- 2+ years
- Category
- Software engineering
- Posted
Skills
Must have
- 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 ML engineering
- Research experience at a frontier AI lab, an AI research or data lab, or substantial independent work; alternatively published research or a released model with strong benchmark results
- Experience with Python, PyTorch, FSDP, FastAPI, GRPO, and distributed GPU clusters
- Experience with post-training methods, training data, reward design, and model evaluation
Full description
About the Role
Join a small applied research team working directly with frontier AI research organizations on challenging post-training problems. This research-first role focuses on reinforcement learning, training data, and experiments that improve model capabilities.
What You'll Do
- Work with customer research teams to turn open-ended 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, 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 determine next steps.
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, an AI research or data lab, or through substantial independent work; alternatively, published research or a released model with strong results on a notable benchmark.
- Experience with Python, PyTorch, FSDP, FastAPI, GRPO, and distributed GPU clusters.
- Experience with post-training methods, training data, reward design, and model evaluation.
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 location.