Research Intern, Efficient Deep Learning - 2027
$38 - $94 USD yearly
Originally posted 5 October 2026 by the employer.
Join the Deep Learning Efficiency Research (DLER) team focusing on efficient deep learning for diffusion language models and agentic AI.
About the role
This Research Intern role focuses on advancing efficient deep learning methods within NVIDIA's Deep Learning Efficiency Research (DLER) team. You will contribute to research concerning efficient diffusion language models, multimodal generative models, and efficient agentic AI with hybrid inference orchestration across cloud and edge.
What you'll do
- Research, design, and implement novel methods for efficient deep learning in diffusion LLMs and multimodal models, including sampling efficiency, adaptive unmasking, self-speculation / parallel decoding, training and distillation pipelines, and multimodal generation.
- Research, design, and implement novel methods for efficient deep learning in efficient agentic AI, including hybrid inference orchestration across cloud and edge, routing and scheduling policies, on-device vs. cloud expert delegation, and resource-aware agent loops.
- Publish original research.
- Collaborate with other team members and teams.
- Work with product groups to transfer technology.
What you'll need
- Pursuing a Ph.D. in Computer Science/Engineering or Electrical Engineering.
- Excellent knowledge of theory and practice of machine learning and deep learning.
- Experience with large language models, diffusion language models, multimodal / vision-language models, or agentic systems.
- Hands-on experience with large-scale model training, including data preparation and model parallelization (tensor and pipeline).
Skills: efficient deep learning, diffusion language models, multimodal generative models, agentic AI, model optimization
This role has been open 0 days — well below the 76-day median for AI/ML Hardware Engineering roles.
AI/ML Hardware Engineering · AI ML Hardware
|
Open roles in category
504
|
Median days open
76 d
|
Median salary
$230k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | NVIDIA | All employers we track in this specialty (504 roles · 75 employers) |
|---|---|---|
| Open roles in this specialty | 85 | 504 |
| Open roles in the wider Software, Firmware & Systems family | 971 | 5996 · 146 employers |
| Median days open | 71 d | 76 d (−5 d vs this employer) |
| Median salary (USD postings) | — | $230k |
Skills observed across this category: efficient deep learning, diffusion language models, multimodal generative models, agentic AI, model optimization
Who's hiring in this category
- Qualcomm · 99 open roles · median 109 d
- NVIDIA (this employer) · 84 open roles · median 72 d
- AMD · 42 open roles · median 66 d
- Micron Technology · 30 open roles · median 58 d
- Mobileye · 18 open roles · median 73 d
- NXP Semiconductors · 15 open roles · median 63 d
How we counted: 504 open AI/ML Hardware Engineering (AI ML Hardware) roles from 75 employers tracked in the SemiconductorJobs index, counted 6 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.