AI Research Scientist, Reinforcement Learning (LLM) and Post-Training - CA, Santa Clara, United States
$178,500 - $306,000 USD yearly
Originally posted 2 July 2026 by the employer. Relisted by the employer on 2 September 2026.
About the role
AMD is hiring an AI Research Scientist specializing in reinforcement learning to advance post-training and interactive learning for large generative models applied to engineering and hardware-adjacent tasks. The focus spans code, optimization, tool use, and long-horizon decision making.
What you'll do
- 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 such as reward hacking, degenerate policies, and instability, proposing experimental mitigations. - Collaborate with RL infra engineers to scale training and define interfaces for rollout generation, logging, and reproducibility. - Publish research at venues such as NeurIPS, ICML, and ICLR while contributing internal technical leadership on the RL roadmap.
What you'll need
- Fluent understanding of RL theory alongside practical experience moving from ablation to production-scale training. - Experience with policy optimization, preference-based methods, exploration, credit assignment, and reward modeling. - Experience addressing reward misspecification, variance reduction, and rigorous empirical evaluation.
Nice to have
- PhD in Computer Science, Machine Learning, or related field. - 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 using GPUs and 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.
Skills: Reinforcement Learning (LLM), post-training, generative models, policy optimization, reward modeling
This role has been open 111 days — well above the 83-day median for AI/ML Hardware Engineering roles.
AI/ML Hardware Engineering · AI ML Hardware
|
Open roles in category
511
|
Median days open
83 d
|
Median salary
$236k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | AMD | All employers we track in this specialty (511 roles · 76 employers) |
|---|---|---|
| Open roles in this specialty | 38 | 511 |
| Open roles in the wider Software, Firmware & Systems family | 406 | 6296 · 145 employers |
| Median days open | 95 d | 83 d (+12 d vs this employer) |
| Median salary (USD postings) | — | $236k |
Skills observed across this category: Reinforcement Learning (LLM), post-training, generative models, policy optimization, reward modeling
Who's hiring in this category
- Qualcomm · 103 open roles · median 112 d
- NVIDIA · 91 open roles · median 56 d
- AMD (this employer) · 40 open roles · median 85 d
- Micron Technology · 27 open roles · median 56 d
- Analog Devices · 18 open roles · median 39 d
- Mobileye · 18 open roles · median 84 d
How we counted: 511 open AI/ML Hardware Engineering (AI ML Hardware) roles from 76 employers tracked in the SemiconductorJobs index, counted 21 Oct 2026. Specialty figures count only roles carrying this exact specialty label, so an employer's related work in neighbouring specialties is not included there — it is counted in the wider Software, Firmware & Systems family row. Figures refresh nightly.