Senior Principal Machine Learning Engineer - San Jose, California, United States
$220,000 - $300,000 USD yearly
Originally posted 16 August 2026 by the employer.
Optimize Large Language Models for SambaNova's RDU accelerators.
About the role
As a Senior Principal Machine Learning Engineer, you will design, develop, and optimize machine learning models with a focus on Large Language Models (LLMs) to run efficiently on SambaNova's RDU architecture. This role bridges advanced LLM research and practical deployment, involving model architecture development and inference efficiency improvements.
What you'll do
- Define and drive technical strategy for ML model development, training pipelines, and inference systems on SambaNova's RDU and hardware ecosystem.
- Lead hardware-software co-design efforts in collaboration with compiler, systems, and hardware teams to shape architectural decisions.
- Serve as the senior technical voice in critical design reviews, architectural decisions, and cross-functional planning.
- Mentor and develop principal and senior ML engineers, elevating technical capabilities through collaboration and knowledge transfer.
- Drive resolution of complex technical challenges, including those that span organizational boundaries or require novel approaches.
What you'll need
- B.S. in Computer Science, Electrical Engineering, or related field.
- 8+ years of industry experience in machine learning engineering, with a record of technical leadership on large-scale ML systems.
- Deep expertise in LLM training, fine-tuning, inference optimization, and evaluation at scale.
- Strong background in ML algorithms, deep learning architectures, and modern training methodologies.
- Demonstrated ability to lead and align cross-functional technical efforts and mentor senior engineers.
- Track record of independently scoping and delivering high-complexity, high-ambiguity technical projects.
Nice to have
- M.S. or Ph.D. in Computer Science, Electrical Engineering, or related field.
- Experience with hardware-software co-design with non-GPU accelerators.
- Publications or open-source contributions in LLM training or inference.
- Experience with speculative decoding, mixture-of-experts, or long-context modeling in production.
- Experience with reinforcement learning for post-training.
- Familiarity with compiler or kernel-level optimization for ML workloads.
Skills: Large Language Models, LLMs, SambaNova, RDU, hardware-software co-design, inference optimization
This role has been open 61 days — well below the 82-day median for AI/ML Hardware Engineering roles.
AI/ML Hardware Engineering · AI ML Hardware
|
Open roles in category
508
|
Median days open
82 d
|
Median salary
$235k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | SambaNova | All employers we track in this specialty (508 roles · 77 employers) |
|---|---|---|
| Open roles in this specialty | 4 | 508 |
| Open roles in the wider Software, Firmware & Systems family | 23 | 6157 · 145 employers |
| Median days open | 98 d | 82 d (+16 d vs this employer) |
| Median salary (USD postings) | — | $235k |
Skills observed across this category: Large Language Models, LLMs, SambaNova, RDU, hardware-software co-design, inference optimization
Who's hiring in this category
- Qualcomm · 100 open roles · median 110 d
- NVIDIA · 91 open roles · median 52 d
- AMD · 42 open roles · median 77 d
- Micron Technology · 27 open roles · median 67 d
- Analog Devices · 19 open roles · median 35 d
- Mobileye · 17 open roles · median 79 d
How we counted: 508 open AI/ML Hardware Engineering (AI ML Hardware) roles from 77 employers tracked in the SemiconductorJobs index, counted 17 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.