Staff ML Engineer — AI Hardware & Systems

RolvaTech; San Jose (CA); On-site; USD 160,000 - 210,000; Full time

Job summary

RolvaTech is seeking an ML engineer who works across the full stack from model to silicon, optimizing training and inference performance on GPU/AI-accelerator infrastructure. You will design, train, and evaluate ML models and take them into production, while adapting to chip constraints and real hardware limits.

You’ll also harden and extend NPU cores into production silicon, build or optimize inference engines, and work with firmware, RTL, and QA teams to ship AI features end-to-end.

Qualifications

• 5–7+ years hands-on AI/ML experience; Master's required, PhD preferred
• Hands-on 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

Responsibilities

• 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

Skills

NPU/AI-accelerator

Systems-level software

Inference engine

Test harness

Cybersecurity

Education

Master's degree in AI/ML or related field

PhD preferred

Job description

RolvaTech is seeking an ML engineer who works across the full stack from model to silicon, optimizing training and inference performance on GPU/AI-accelerator infrastructure. You will design, train, and evaluate ML models and take them into production, while adapting to chip constraints and real hardware limits.

You’ll also harden and extend NPU cores into production silicon, build or optimize inference engines, and work with firmware, RTL, and QA teams to ship AI features end-to-end.