Advanced Micro Devices, Inc

Research Scientist, Reinforcement Learning (LLM) and Post-training

Advanced Micro Devices, Inc • $130K — $180K *
Santa Clara, CA 95051In-Person
Consumer Technology
Less than 5 years of experience
Today
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Job Overview by Ladders

Qualifications

  • PhD in Computer Science, Machine Learning, or a related field preferred.
  • Strong publication record in reinforcement learning or related areas.
  • Hands-on experience training RL or preference-optimized models at scale.
  • Experience with LLM post-training and RLHF/RLAIF methods.
  • Familiarity with compilers, kernels, EDA workflows, or large codebases is a plus.

Responsibilities

  • Research and develop reinforcement learning methods for post-training LLMs and code models.
  • Design reward models, curricula, and training recipes for sparse or expensive labels.
  • Characterize failure modes and propose mitigations grounded in experiments.
  • Collaborate with infra engineers to scale training processes.
  • Publish research at top venues and provide internal leadership on the RL roadmap.

Benefits

  • Comprehensive health and wellness plans.
  • Paid time off and flexible working hours.
  • Opportunities for professional development and training.
  • Collaborative and innovative work environment.
Full Job Description
THE ROLE:

We are hiring a 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.


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About Advanced Micro Devices, Inc

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Learn more about Advanced Micro Devices, Inc
Size
15,500 employees
Market Cap
$100.9 billion
Industry
Manufacturing & Automotive
Net Income
$2.4 billion
Founded
1969
5 Year Trend
+30.9%
Revenue
$9.7 billion
NASDAQ
AMD
* Ladders Estimates

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