Senior ML Engineer — AI Silicon & Security Platform

ValueBase Consulting LLC; San Jose (CA); On-site; USD 180,000 - 280,000; Full time

Job summary

ValueBase Consulting LLC seeks an experienced ML engineer in California to work across the full stack from model to silicon, optimizing training and inference on GPU/AI-accelerator infrastructure and adapting models to the constraints of the chip. You will move fluidly between algorithm design, systems software, and hardware verification, enabling end-to-end AI features on real hardware.

The role requires 8-10+ years in AI/ML, a Masters, and hands-on experience with GPU clusters, MLOps, and

Qualifications

• 8-10+ years hands-on AI/ML experience.
• Master's degree required; PhD preferred.
• Experience with AI/ML infrastructure and performance on GPU/accelerator hardware.
• Experience taking models into production: data pipelines and deployment.
• AI chip/hardware-aware ML performance optimization.
• Deep expertise in at least 2 of the five specialty areas listed.

Responsibilities

• Optimize training and inference performance across GPU/AI-accelerator infra; include MLOps pipelines.
• Design, train, and evaluate ML models (DL/LLM/CV/recommendation) for production.
• Harden and extend NPU cores into production silicon.
• Build or optimize inference engines and runtimes against real hardware constraints.
• Work below application layer—BMC firmware, embedded Linux, RTOS—so AI features run reliably.
• Create automated test/verification harnesses for RTL/DV, hardware bring-up, or manufacturing test.
• Apply ML to security: AI-driven log/intrusion analysis or firmware/hardware security work.
• Collaborate with RTL/hardware, firmware, and QA to ship AI features end-to-end.

Skills

AI/ML engineering

GPU optimization

MLOps pipelines

Production deployment

NPU/ASIC awareness

Education

Master's degree

PhD preferred

Tools

TensorRT

RVV/CoralNPU

Zephyr RTOS

Linux

Job description

ValueBase Consulting LLC seeks an experienced ML engineer in California to work across the full stack from model to silicon, optimizing training and inference on GPU/AI-accelerator infrastructure and adapting models to the constraints of the chip. You will move fluidly between algorithm design, systems software, and hardware verification, enabling end-to-end AI features on real hardware.

The role requires 8-10+ years in AI/ML, a Masters, and hands-on experience with GPU clusters, MLOps, and