Senior Staff Machine Learning Engineer

ValueBase Consulting is seeking a Senior Staff Machine Learning Engineer to support an AI-enhanced security processor effort. This contract role spans the full lifecycle, from model development and MLOps to silicon/NPU enablement, inference runtime optimization, and embedded integration with end-to-end deployment and monitoring.

Key Responsibilities

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

Required 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, distributed training, inference-serving optimization, and MLOps pipelines
• Experience in model and algorithm development, including designing, training, and evaluating ML models
• Experience taking models into production, including feature engineering, data pipelines, and deployment
• AI chip and hardware-aware ML experience, such as optimizing inference engines for a specific chip or adapting model architecture and quantization to chip constraints
• Deep, hands-on expertise in at least 2 of the following specialty areas:
  • NPU / AI-ACCELERATOR: hardening or extending an NPU core into production silicon, mapping models onto MAC or 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: 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, such as an agent-driven RTL/DV test runner or a hardware bring-up or manufacturing test harness
  • CYBER SECURITY: AI-driven log or intrusion analysis, AI-assisted penetration testing, or firmware or hardware security

Relevant Technologies

• 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

Location and Hiring Constraints

• San Jose, CA, USA (onsite)
• Candidates should be located in California; W2 candidates are encouraged to apply
• Any visa is fine

Compensation and Contract Details

• Contract duration: 12 Months
• Pay rate: USD 70 per hour on W2 or USD 80 per hour on C2C