AI Researcher - Reinforcement Learning & Post-Training

surge group • New York City Metropolitan Area

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AI Summary

Design and execute post-training experiments using reinforcement learning, supervised fine-tuning, and preference optimization to enhance model capabilities. Develop reward functions, verifiers, and evaluation frameworks to distinguish real capability gains from reward hacking. Requires 2-7+ years of applied research experience with substantive reinforcement learning and practical Python/PyTorch skills.

Key Highlights

Collaborate directly with frontier research teams to translate ambitious objectives into concrete technical deliverables.

Shape training environments and trajectory data to investigate how correctness and data quality affect model behavior.

Analyze training dynamics across distributed GPU workloads to guide subsequent research directions.

Key Responsibilities

Design and run post-training experiments using reinforcement learning, supervised fine-tuning and preference optimization.

Develop reward functions, verifiers and evaluation frameworks that distinguish real capability gains from reward hacking.

Shape training environments and trajectory data, investigating how correctness, coverage and data quality affect model behavior.

Analyze training dynamics and experimental results across distributed GPU workloads, using the findings to guide subsequent research.

Collaborate directly with frontier research teams, translating ambitious research objectives into concrete technical deliverables.

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Technical Skills Required

Reinforcement Learning Python PyTorch

Benefits & Perks

$300,000–$500,000 base salary

Meaningful equity

Visa sponsorship and transfers supported

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Nice to Have

RL-focused PhD

Published research or a released model with strong results on a recognized benchmark

Job Description

AI Researcher - Reinforcement Learning & Post-Training

$300,000–$500,000 base + meaningful equity

New York, San Francisco or Seattle | Hybrid, three days in office

We’re partnering with a well-funded, early-stage AI research company working directly with leading frontier labs on some of their hardest reinforcement learning and post-training problems.

The team develops the environments, trajectory datasets and training signals that help models acquire new capabilities. The research centres on a practical question: what makes a model meaningfully better, and how do you prove it?

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