Principal Engineer, Efficient GenAI - CA, San Jose, United States
Originally posted 20 August 2026 by the employer. Relisted by the employer on 25 September 2026.
Specialize in Generative AI training and inference at scale on AMD devices.
About the role
The AI Models and Applications team at AMD is looking for a specialized Principal Engineer to enable innovative and efficient Generative AI training and inference at scale. This role focuses on scaling training and inference for the latest Generative AI models.
What you'll do
- Propose and apply techniques for training and inference, including transformer architectures, parallelism strategies, speculative decoding, and KV-caching strategies.
- Implement novel architectures for Generative AI models for training and inference on AMD platforms.
- Work with open-source frameworks (e.g., PyTorch, JAX, vLLM, SGLang) to integrate AMD-optimized models and libraries.
- Collaborate with software and hardware teams to co-optimize end-to-end performance on AMD solutions.
- Increase adoption of agentic workflows for optimizing and deploying Generative AI applications on AMD platforms.
- Publish and promote work at external venues, including major conferences.
What you'll need
- Deep technical understanding and hands-on experience with Generative AI applications (large language models (LLMs), 3D World and Action Models, or image/video generation models).
- Experience training models at scale.
- Strong technical expertise in Generative AI model training and inference, with familiarity working with deep learning frameworks such as PyTorch, JAX, vLLM, SGLang, and MuJoCo.
- Strong technical expertise in algorithmic innovation for efficient Generative AI applications across both training and inference.
- Experience productizing Generative AI models and training foundation models at scale.
- PhD or master’s degree in computer science, Electrical Engineering, Mathematics, or a related field.
Nice to have
- Expertise and publications in one or more of the following preferred areas: efficient model architectures, optimized training, innovative parallelism strategies, or low-precision training.
- Publications presented at conferences such as NeurIPS, CVPR, ECCV, ICCV, ICML, or ICLR.
- Several years of experience in AI, deep learning, and related software development.
Skills: Physical AI, GPU-accelerated pipelines, simulation, synthetic data generation, VLA Models, WAMs
This role has been open 57 days — well below the 81-day median for AI/ML Hardware Engineering roles.
AI/ML Hardware Engineering · AI ML Hardware
|
Open roles in category
506
|
Median days open
81 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 (506 roles · 76 employers) |
|---|---|---|
| Open roles in this specialty | 42 | 506 |
| Open roles in the wider Software, Firmware & Systems family | 409 | 6157 · 145 employers |
| Median days open | 76 d | 81 d (−5 d vs this employer) |
| Median salary (USD postings) | — | $235k |
Skills observed across this category: Physical AI, GPU-accelerated pipelines, simulation, synthetic data generation, VLA Models, WAMs
Who's hiring in this category
- Qualcomm · 102 open roles · median 107 d
- NVIDIA · 90 open roles · median 51 d
- AMD (this employer) · 41 open roles · median 79 d
- Micron Technology · 27 open roles · median 66 d
- Analog Devices · 19 open roles · median 34 d
- Mobileye · 17 open roles · median 78 d
How we counted: 506 open AI/ML Hardware Engineering (AI ML Hardware) roles from 76 employers tracked in the SemiconductorJobs index, counted 16 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.