7 hours ago
San Jose, CA, USAStaff+
Responsibilities
- Optimize training and inference performance across GPU and AI-accelerator infrastructure, including MLOps pipelines.
- Design, train, evaluate, and productionize machine learning models for deep learning, LLM, computer vision, or recommendation systems.
- Harden and extend NPU cores such as CoralNPU into production silicon.
- Build or optimize inference engines and serving runtimes for latency, memory, power, and other hardware constraints.
- Develop or modify BMC firmware, embedded Linux, or RTOS software so AI features run reliably on physical systems.
- Build automated test and verification harnesses for AI-assisted RTL/DV, hardware bring-up, or manufacturing testing.
- Apply ML to log and intrusion analysis, penetration testing, and firmware or hardware security.
- Collaborate with RTL, hardware, firmware, and QA teams to ship AI features from training through deployment and monitoring.
Requirements
- 5–7+ 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 designing, training, and evaluating ML models and taking models into production through feature engineering, data pipelines, and deployment.
- AI chip or hardware-aware ML experience, such as optimizing an inference engine for a specific chip or adapting model architecture or quantization to chip constraints.
- Deep hands-on expertise in at least two specialty areas: NPU/AI accelerators, systems software, inference engines/runtimes, test and verification harnesses, or cybersecurity.
Categories
About Axiado
Axiado builds hardware-anchored platform security for servers and infrastructure, centered on its Trusted Control/Compute Unit (TCU) processor with root-of-trust functions and AI-based threat detection. It sells security silicon, firmware, and reference platforms to server and network equipment makers and cloud providers integrating baseboard management and platform resiliency. The company was founded in 2017, is privately held, and is headquartered in San Jose, California.