Applied RL Researcher - Post-Training & Evaluation

Confidential; New York (NY); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Confidential is hiring for a hybrid, US-based research role focused on reinforcement learning in collaboration with frontier AI labs. You will be hands-on designing environments, datasets and experiments that advance model capability, with emphasis on RL training data and post-training methods.

This is a research-heavy position joining two applied researchers to meet fast-growing customer demand. Sponsorship is available, and the team values deep RL understanding over pure engineering execution.

Qualifications

• 2+ years of experience as an applied researcher or research scientist.
• Hands-on RL work at a frontier lab or equivalent with demonstrable results.
• Deep understanding of RL and training data beyond tooling.
• Experience with post-training methods such as GRPO, preference optimization and SFT.

Responsibilities

• Collaborate with customer research labs to translate goals into concrete environments, datasets and experiments.
• Design and run post-training loops and identify signals that improve model performance.
• Shape high‑fidelity trajectories and training data with attention to correctness and edge cases.
• Build reward functions, validators and eval harnesses to resist reward hacking and show capability gains.
• Analyze results across distributed GPU clusters to decide next steps.

Skills

Applied researcher

Reinforcement learning

RL training data

Post-training methods

Research focus

Tools

GRPO

SFT

Preference optimization

Job description

Confidential is hiring for a hybrid, US-based research role focused on reinforcement learning in collaboration with frontier AI labs. You will be hands-on designing environments, datasets and experiments that advance model capability, with emphasis on RL training data and post-training methods.

This is a research-heavy position joining two applied researchers to meet fast-growing customer demand. Sponsorship is available, and the team values deep RL understanding over pure engineering execution.