Summer 2027 PhD Gen AI and Reinforcement Learning Research Intern - CA, Santa Clara, United States
Originally posted 21 September 2026 by the employer — open 0 days.
About the role
This role involves research and development of methods for post-training large generative models, including exploration of policy optimization, preference learning, reward modeling, and credit-assignment techniques. The intern will develop evaluations to measure model quality, robustness, safety, and real-world task performance.
What you'll do
- Research and prototype methods for post-training large generative models.
- Explore policy optimization, preference learning, reward modeling, exploration, and credit-assignment techniques.
- Develop methods for improving reasoning, code generation, tool use, and agentic behavior.
- Design and run controlled experiments using verifiable, preference-based, or model-generated feedback.
- Analyze failure modes such as reward hacking, policy degeneration, and training instability.
- Develop evaluations that measure model quality, robustness, safety, and real-world task performance.
What you'll need
- Currently pursuing a PhD in Computer Science, Machine Learning, Artificial Intelligence, Electrical Engineering, or Computer Engineering.
- Strong knowledge of reinforcement learning and modern deep-learning methods.
- Experience implementing and evaluating machine-learning models using Python and PyTorch.
- Familiarity with LLM post-training, RLHF/RLAIF, preference optimization, or language and multimodal agents.
- Experience conducting reproducible experiments and analyzing empirical results.
Nice to have
- Publications at machine-learning or computer-vision conferences such as ICML, NeurIPS, ICLR, CVPR, ICCV, or ECCV.
- Experience with large-scale model training, GPU computing, or distributed systems.
- Familiarity with generative AI, multimodal learning, reasoning models, code models, or agentic systems.
Skills: Generative AI, Reinforcement Learning, LLM post-training, policy optimization, PyTorch, machine-learning models
This role has been open 0 days — well below the 67-day median for AI/ML Hardware Engineering roles.
AI/ML Hardware Engineering · AI ML Hardware
|
Open roles in category
522
|
Median days open
67 d
|
Median salary
$235k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | AMD | All employers we track in this specialty (522 roles · 76 employers) |
|---|---|---|
| Open roles in this specialty | 46 | 522 |
| Open roles in the wider Software, Firmware & Systems family | 399 | 5873 · 143 employers |
| Median days open | 60 d | 67 d (−7 d vs this employer) |
| Median salary (USD postings) | — | $235k |
Skills observed across this category: Generative AI, Reinforcement Learning, LLM post-training, policy optimization, PyTorch, machine-learning models
Who's hiring in this category
- Qualcomm · 107 open roles · median 97 d
- NVIDIA · 86 open roles · median 68 d
- AMD (this employer) · 46 open roles · median 60 d
- Micron Technology · 32 open roles · median 40 d
- Mobileye · 25 open roles · median 54 d
- Analog Devices · 14 open roles · median 25 d
How we counted: 522 open AI/ML Hardware Engineering (AI ML Hardware) roles from 76 employers tracked in the SemiconductorJobs index, counted 22 Sept 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.