Member of Technical Staff, Applied Researcher

True Talent Solutions Partners; New York (NY); Hybrid; USD 300,000 - 500,000; Full time

Benefits offered by this job

Visa sponsorship available

Job summary

True Talent Solutions Partners is seeking an experienced applied researcher to work with frontier AI research labs on post-training problems. You will translate open-ended research goals into experiments and training data to improve model capabilities.

The role involves designing RL loops, building evaluation harnesses, and analyzing results across distributed GPU clusters to guide future research in a hybrid New York environment, with Seattle as an additional listed location.

Qualifications

• 2+ years as an applied researcher or research scientist with hands-on model training experience.
• Practical reinforcement learning and agent experience beyond standard ML engineering work.
• Research experience at frontier AI labs, emerging labs, or notable publications.
• Strong understanding of reinforcement learning and training data with demonstrated outcomes.
• Proficiency in Python, PyTorch, FSDP, FastAPI, GRPO, and distributed GPU clusters.

Responsibilities

• Partner with customer research teams to translate 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 training signals that improve model performance and shape high-fidelity trajectories and datasets with attention to edge cases.
• Build reward functions, rubrics, validators, and evaluation harnesses that resist reward hacking.
• Analyze post-training signals and experiment results across distributed GPU clusters to guide subsequent research.

Skills

Applied researcher

Reinforcement learning

Python

Distributed GPU clusters

Research publications

Tools

PyTorch

GRPO

FastAPI

FSDP

Job description

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.