Senior Machine Learning Engineer

Value Base 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.

• 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
  .

• 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.

• 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
  .

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
.