RL Research Scientist for Post-Training LLMs & Code Models

Advanced Micro Devices; Santa Clara, Northern (CA, KY); Hybrid; USD 170,000 - 250,000; Full time

Job summary

Advanced Micro Devices (AMD) is hiring an AI Research Scientist specializing in reinforcement learning for post-training and interactive learning with large generative models used in demanding engineering tasks. You will invent and analyze RL methods, design reward models, and collaborate with infra and product teams to land methods that improve task success while maintaining stability and safety.

You will publish and ship, balancing theory with practical production-scale training, focusing on

Qualifications

• Strong publication record in reinforcement learning or closely related ML areas.
• Hands-on experience training RL or preference-optimized models at non-trivial scale (GPUs, distributed jobs).
• Experience with LLM post-training, RLHF/RLAIF, or policy optimization for language or code agents.
• Familiarity with compilers, kernels, EDA-style workflows, or large-scale codebases is a plus.

Responsibilities

• Research and develop RL methods for post-training LLMs and code models on structured engineering tasks with verifiable or preference-based feedback.
• Design reward models, curricula, and off-policy or on-policy training recipes suited to sparse, noisy, or expensive labels from experts and simulators.
• Characterize failure modes (reward hacking, degenerate policies, instability) and propose mitigations grounded in experiments.
• Collaborate with RL infra engineers to scale training; define interfaces for rollout generation, logging, and reproducibility.
• Publish at top venues (e.g. NeurIPS, ICML, ICLR) and contribute internal technical leadership on the RL roadmap.

Skills

Reinforcement learning

LLMs

Policy optimization

Reward modeling

Education

PhD in Computer Science or related field

Job description

Advanced Micro Devices (AMD) is hiring an AI Research Scientist specializing in reinforcement learning for post-training and interactive learning with large generative models used in demanding engineering tasks. You will invent and analyze RL methods, design reward models, and collaborate with infra and product teams to land methods that improve task success while maintaining stability and safety.

You will publish and ship, balancing theory with practical production-scale training, focusing on