Senior Staff Machine Learning Engineer

ValueBase Consulting; San Jose (CA); On-site; USD 96,000 - 110,000; Full time

Job summary

ValueBase Consulting in San Jose, CA is hiring a Senior Staff Machine Learning Engineer for an onsite contract role. You will work at the intersection of AI and security processor hardware, spanning model development through silicon/NPU enablement, inference engines, and end-to-end MLOps deployment.

You’ll optimize hardware-aware performance and verification workflows to ensure AI features run reliably on real systems.

Qualifications

• 8-10+ years of hands-on AI/ML experience; Master’s degree required; PhD preferred.
• Hands-on experience with AI/ML infrastructure and performance, including GPU clusters and distributed training.
• Experience taking models into production, including feature engineering, data pipelines, and deployment.

Responsibilities

• Optimize training and inference performance across GPU and AI-accelerator infrastructure, including MLOps pipelines.
• Design, train, and evaluate ML models, including deep learning, LLMs, computer vision, or recommendation systems; drive to production.
• Harden and extend NPU cores into production silicon with RVV/tensor core considerations.
• Build or optimize inference engines and serving runtimes for real hardware constraints focusing on latency, memory, and power.
• Work with BMC firmware, embedded Linux, or RTOS to ensure AI features run reliably on hardware.
• Create automated test and verification harnesses supporting RTL/DV, hardware bring-up, or MFG tests.
• Apply ML to security uses cases such as AI-driven log or intrusion analysis and firmware security work.
• Collaborate with RTL/hardware, firmware, and QA teams to ship AI capabilities end-to-end.

Skills

AI infrastructure

GPU clusters

MLOps pipelines

Model development

Production deployment

Security-aware ML

Education

Master’s degree in a relevant field

PhD preferred

Tools

NPU hardware

RVV/tensor core

CoralNPU

Job description

ValueBase Consulting is hiring a Senior Staff Machine Learning Engineer for an onsite contract role in San Jose, CA. If you want to work at the intersection of AI and security processor hardware, this position spans the full lifecycle from model development through silicon/NPU enablement, inference engines, and end-to-end MLOps deployment. You’ll also contribute to hardware-aware performance optimization and verification workflows that help AI features run reliably on real systems.

What you’ll do

• Optimize training and inference performance across GPU and AI-accelerator infrastructure, including MLOps pipelines.
• Design, train, and evaluate ML models, including options such as deep learning, LLMs, computer vision, or recommendation systems, and drive them into production.
• Harden and extend NPU cores (for example, building on an open RVV/tensor core like CoralNPU) into production silicon.
• Build or optimize inference engines and serving runtimes for real hardware constraints, with focus on latency, memory, and power.
• Work below the application layer when needed, including BMC firmware, embedded Linux, or RTOS such as Zephyr, so AI features behave reliably on actual platforms.
• Create automated test and verification harnesses that support AI-assisted RTL/DV, hardware bring-up, or manufacturing test loops.
• Apply ML to security, including use cases such as AI-driven log or intrusion analysis, AI-assisted penetration testing, or firmware and hardware security work.
• Collaborate closely with RTL/hardware, firmware, and QA teams to ship AI capabilities end-to-end, including deployment and monitoring.

What you bring

• 8-10+ years of hands-on AI/ML experience. Master’s degree required; PhD preferred.
• Hands-on experience with AI/ML infrastructure and performance, including GPU clusters, distributed training, inference-serving optimization, and MLOps pipelines.
• Model and algorithm development experience, including designing, training, and evaluating ML models.
• Experience taking models into production, including feature engineering, data pipelines, and deployment.
• AI chip / hardware-aware ML experience, including optimizing inference for a specific chip or adapting model architecture and quantization to meet chip constraints.
• Deep, hands-on expertise in at least 2 of the following specialty areas (not all five required):
  • NPU / AI-ACCELERATOR: hardening or extending an NPU core into production silicon, mapping models onto MAC/tensor-engine constraints, or NPU-aware RTL/DV work.
  • SYSTEMS / SYS-LEVEL SOFTWARE: BMC firmware, embedded Linux, RTOS (for example, Zephyr), or other low-level system software.
  • INFERENCE ENGINE / RUNTIME: building or materially optimizing an inference engine or serving runtime against real hardware constraints.
  • TEST / VERIFICATION HARNESS: building an automated harness that closes a loop, such as an agent-driven RTL/DV test runner or a hardware bring-up or MFG test harness.
  • CYBER SECURITY: AI-driven log or intrusion analysis, AI-assisted penetration testing, or firmware and hardware security.

Role details

• Location: San Jose, CA, USA (onsite)
• Employment type: Contract
• Duration: 12 Months
• Compensation: USD 70-80 per hourly
• W2: $70/HR on W2
• C2C: $80/HR on C2C
• Hiring constraints: Need candidates who are in California. W2 candidates are encouraged to apply. Any visa is fine.

Technologies you may work with

GPU, AI-accelerator, MLOps pipelines, NPU, open RVV/tensor core, CoralNPU, BMC firmware, embedded Linux, RTOS, Zephyr, AI-assisted RTL/DV, RTL/DV, LLM, CV.