RL Research Scientist for Post-Training LLMs & Code Models

Advanced Micro Devices, Inc.; Santa Clara (CA); 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

ADVANCE YOUR CAREER. ADVANCE THE WORLD.

At AMD, we believetechnology has the power to solve the world’s most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMDis shapingthefuture.

Whetheryou’redesigning next-gen processors, enabling AI breakthroughs, orbringing leading edge products to market, every role at AMD contributes to something bigger— technologythat moves the world forward.Join us and, together, we’ll advance your career.

THE ROLE:

We are hiring a AI Research Scientist, Reinforcement Learning (LLM) and Post-Training, specializing in reinforcement learning to advance post-training and interactive learning for large generative models applied to demanding engineering and hardware-adjacent tasks (code, optimization, tool use, and long-horizon decision making). You will invent and analyze RL algorithms—policy optimization, preference-based methods, exploration, credit assignment, and reward modeling—run rigorous empirical studies, and partner with infra and product teams to land methods that improve measurable task success without sacrificing stability or safety.

THE PERSON:

You publish and ship. You are fluent in both RL theory and the practical path from ablation to production-scale training. You care about reward misspecification, variance reduction, and evaluation that reflects real constraints—not only toy environments.

KEY 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

PREFERRED EXPERIENCE:

Strong publication record in reinforcement learning or closely related machine learning 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

ACADEMIC CREDENTIALS:

• PhD in Computer Science, Machine Learning, or related field strongly preferred.

#LI-BM1

#LI-Hybrid

Benefits offered are described: AMD benefits at a glance.

AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.

AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.

This posting is for an existing vacancy.