AI Research Scientist, Reinforcement Learning (LLM) and Post-Training

Amd

Confirmed live today High trust
Hybrid

Quick summary

Work type
Hybrid
Location
Santa Clara, CA
Salary
$204,000–$306,000 / yr
Posted
55 days ago
Freshness
Confirmed live today
Closes
Sep 1, 2027
Nearby
99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $221k
This role $255k
$159k $322k
below market most similar roles pay here above market

This role pays more than 81% of similar roles. Most pay $187,500–$254,750 — the blue band above. At the midpoint, this role pays about $255k versus about $221k for comparable roles.

Based on 240 similar postings.

Employer

About Amd

AMD (Advanced Micro Devices) is a semiconductor company that develops high-performance processors, graphics cards, and adaptive computing solutions for gaming, data centers, and embedded markets. Industry: Semiconductors

Amd currently has 496 open roles on FindRole.

Listed pay typically runs $172,000–$258,000 across 496 roles with salary data.

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At a glance

TL;DR · AI Research Scientist, Reinforcement Learning (LLM) and Post-Training

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The AI Research Scientist, Reinforcement Learning (LLM) and Post-Training will join the team to advance post-training and interactive learning for large generative models. This role focuses on demanding engineering and hardware-adjacent tasks, including code, optimization, tool use, and long-horizon decision making. You will invent and analyze RL algorithms such as policy optimization, preference-based methods, exploration, credit assignment, and reward modeling. Key responsibilities include designing reward models, curricula, and training recipes for sparse or noisy labels while characterizing failure modes like reward hacking. You will collaborate with infrastructure engineers to scale training and publish at top venues like NeurIPS. Required skills include RL theory, experience with RLHF/RLAIF, and training models at scale using GPUs. Familiarity with compilers, kernels, or EDA-style workflows is also preferred.

Skills

Reinforcement Learning LLM RLHF RLAIF Policy Optimization Reward Modeling GPUs Compilers Kernels EDA Machine Learning

What you'll do

  • Research and develop RL methods for post-training LLMs and code models on structured engineering tasks.
  • Design reward models, training curricula, and on-policy or off-policy training recipes for sparse or noisy labels.
  • Characterize failure modes like reward hacking and instability and propose mitigations through experiments.
  • Partner with infrastructure engineers to scale training and define interfaces for rollout generation and logging.
  • Publish research at top venues and provide internal technical leadership on the RL roadmap.
  • Conduct empirical studies to improve measurable task success while maintaining model stability and safety.

What we're looking for

  • Research and develop RL methods for post-training LLMs and code models on structured engineering tasks.
  • Design reward models, curricula, and training recipes for sparse, noisy, or expensive labels.
  • Characterize failure modes like reward hacking and propose mitigations grounded in experiments.
  • Collaborate with RL infra engineers to scale training and define interfaces for rollout generation.
  • Publish at top venues like NeurIPS, ICML, and ICLR.
  • Strong publication record in reinforcement learning or closely related machine learning areas (preferred).
  • Hands-on experience training RL or preference-optimized models at non-trivial scale (preferred).
  • Experience with LLM post-training, RLHF/RLAIF, or policy optimization for language or code agents (preferred).

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