Senior Staff Machine Learning Engineer

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

Need candidates who are in California. W2 candidates are encouraged to apply.

Client is an AI-enhanced security processor company redefining the control and management of every digital system. The company builds silicon-rooted security and management chips - including the TCU (Trusted Control/Compute Unit) - for AI data center infrastructure, combining platform security, BMC/firmware, and on-chip AI for real-time threat detection and dynamic power/thermal management.

Job Description

About the role

We're looking for an ML engineer who works across the full stack from model to silicon - comfortable optimizing training and inference performance on GPU/AI-accelerator infrastructure, building or tuning models, and adapting model and inference-engine design to the constraints of the underlying chip and its NPU. You'll move fluidly between algorithm work, systems-level software, and infrastructure work, closing the loop end-to-end rather than owning just one layer of the stack. This is a rare chance to work the full cycle of AI silicon, from model down to chip - something most ML engineers at large companies never get access to.

What you'll do

• Optimize training and inference performance across GPU and AI-accelerator infrastructure, including MLOps pipelines
• Design, train, and evaluate ML models (deep learning, LLM, CV, or recommendation systems) and take them into production
• Harden and extend NPU cores (e.g. building on an open RVV/tensor core like CoralNPU) into production silicon
• Build or optimize inference engines and serving runtimes against real hardware constraints - latency, memory, and power
• Work below the application layer where needed - BMC firmware, embedded Linux, or RTOS (e.g. Zephyr) - so AI features run reliably on real systems
• Build automated test/verification harnesses that close the loop for AI-assisted RTL/DV, hardware bring-up, or manufacturing test
• Apply ML to security - AI-driven log/intrusion analysis, AI-assisted penetration testing, or firmware/hardware security work
• Collaborate closely with RTL/hardware, firmware, and QA teams to ship AI features end-to-end, from training through deployment and monitoring

Requirements

• 8-10+ years of hands-on AI/ML experience; Masters required, PhD preferred
• Hands-on experience with AI/ML infrastructure and performance - GPU clusters, distributed training, inference-serving optimization, MLOps pipelines
• Model / algorithm development experience - designing, training, and evaluating ML models
• Experience taking models into production - feature engineering, data pipelines, deployment
• AI chip / hardware-aware ML experience - optimizing inference engines for a specific chip, or adapting model architecture/quantization to chip constraints
• Deep, hands-on expertise in at least 2 of the following 5 specialty areas - we don't expect all five:

Specialty Areas

• 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 (e.g. Zephyr), or other low-level system software
• Inference engine / runtime - built or materially optimized an inference engine or serving runtime against real hardware constraints
• Test / verification harness - built an automated harness that closes a loop, e.g. an agent-driven RTL/DV test runner or a hardware bring-up / MFG test harness
• Cyber security - AI-driven log/intrusion analysis, AI-assisted penetration testing, or firmware/hardware security

Job Category

Information Technology