Applied AI Engineer, Top Hardware Priorities - CA, Santa Clara, United States

Originally posted 8 July 2026 by the employer.

About the role

Applied AI Engineers work directly with hardware and software engineering teams on high-priority AI-for-engineering efforts, turning difficult engineering workflows into AI-assisted systems. The role covers hardware-adjacent domains such as design optimization, verification, simulation, firmware, performance debugging, routing, issue triage, and compute library optimization.

What you'll do

- Build applied AI workflows for hardware and software engineering priorities, including optimization, verification, debugging, simulation, and automation workflows. - Convert manual engineering processes into structured tasks with inputs, candidate generation, validation, scoring, logging, and reproducible comparisons. - Partner with domain owners to define success metrics including correctness, performance, resource usage, quality, latency, coverage, engineer time saved, or issue classification accuracy. - Develop tools that let AI agents use compilers, simulators, formal checks, profilers, benchmark harnesses, ticket systems, and engineering knowledge sources. - Build human-in-the-loop workflows for tasks requiring annotated data, expert judgment, or subjective triage. - Improve model and agent performance through prompts, tool interfaces, retrieval, evaluation datasets, graders, and structured feedback.

What you'll need

- Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or related field, or equivalent practical experience. - Software engineering experience in Python and at least one systems language such as C++, C, HIP, CUDA, or Rust. - Experience building applied AI, ML, agentic, automation, or developer tooling systems for technical users. - Experience working with engineering tools, logs, tests, benchmark harnesses, and validation workflows. - Debugging and root-cause analysis skills across software systems, AI workflows, or hardware-adjacent tooling.

Nice to have

- Master's degree or PhD with relevant AI, systems, EDA, or hardware/software co-design experience. - Experience with hardware design or verification workflows, including RTL, Verilog/SystemVerilog, simulation, formal verification, EDA tools, timing analysis, or power/performance/area tradeoffs. - Experience with GPU/CPU performance engineering, compiler tooling, profilers, kernel optimization, ROCm/HIP, CUDA, or benchmarking. - Experience designing evaluation datasets, graders, dashboards, leaderboards, or experiment tracking systems. - Familiarity with LLM agents, tool use, retrieval, LLM-as-judge workflows, RL, or post-training methods. - Experience working with internal engineering customers, forward-deployed teams, or cross-functional product engineering programs.

Skills: AI-for-engineering, hardware design and software optimization, agentic systems, validation, GPU/CPU performance, EDA tools