Staff Hardware AI Engineer

Arm Limited; United Kingdom; Remote; GBP 90,000 - 150,000; Full time

Job summary

Arm Limited is seeking a passionate Staff Hardware AI Engineer to join our PE AI team, building AI-enabled automation for hardware design and verification workflows. You will bridge hardware domain knowledge with AI tooling, collaborating with Architects to apply scalable AI solutions that improve cycle time and engineering productivity across Arm's hardware projects.

You’ll prototype fast, evaluate AI tools, and help drive adoption through demos, workshops, and onboarding guides, shaping how

Qualifications

• Deep understanding of RTL design, interfaces RTL/testbench/Verification IP, and debugging techniques.
• Experience with at least one major EDA vendor toolset.
• Practical experience using AI for engineering automation and tooling.
• Clear, concise written communication to capture requirements.

Responsibilities

• Contribute hands-on toward AI-based solutions for Hardware Design & Verification automation.
• Curate/develop solutions using OpenAI Codex skills and tool integrations for flows and analysis.
• Collaborate with Arm Engineering teams to translate real struggles into clear requirements.
• Build quick prototypes and evaluate EDA AI tools to validate feasibility and impact.
• Drive adoption via demos, onboarding guides, and removal of blockers.

Skills

RTL design

Verification IP

Debugging

AI tooling

Written communication

Tools

EDA toolset

Job description

Job Overview:

We’re looking for a passionate Staff Hardware AI Engineer to join our team.

You will build AI-enabled automation that makes Arm digital hardware design & verification engineers drastically improve efficiency across their work assignments thereby improving Arm's cycle time. You’ll bridge deep hardware engineering domain knowledge with AI-based tooling and partner with Architects to apply AI-powered new tools and techniques, at-scale, and transform the day to day working of Arm engineering team.

About the Team

We are the PE AI team, and we focus on improving engineering productivity through smart technology. We work across hardware and software projects, partnering with teams to embed AI into their workflows.

Responsibilities:

• Contribute hands-on toward the development of AI-based solutions for Hardware Design & Verification automation.
• Curate/develop solutions based on OpenAI Codex skills and tool integrations (e.g., simulation flows, log analysis, code review/refactor support).
• Collaborate with Arm Engineering teams to capture real engineering struggles and turn them into clear requirements.
• Build quick prototypes and evaluate EDA AI tools, to validate feasibility and quantify impact.
• Drive adoption as a change leader: run demos/workshops; Contribute onboarding guides; Identify and remove adoption blockers.

Required Skills and Experience:

• Hands on experience with design or verification methodology: Deep understanding of RTL design, interfaces between RTL, testbench and Verification IP, experience with debugging compile and simulation issues.
• Experience with EDA vendor tooling: Domain knowledge of at least one (of the three major) EDA vendor toolset.
• AI tooling fluency: Practical experience using AI for engineering automation (prompt design/iteration, structured outputs, tool use).
• Strong solving attitude: you can quickly diagnose why something doesn’t work in a real environment and drive resolution across teams.
• Clear written communication: can turn ambiguous conversations into requirements and actionable plans.

Nice To Have Skills and Experience:

• Automation / scripting: Strong in at least one scripting language (Python preferred). Ability to integrate and orchestrate tools (parsing logs/reports, running flows, collecting results).
• Experience in an Application Engineer / internal customer-facing role where you translated user needs into working solutions.
• Familiarity with agentic workflows, skills, tool calling patterns

What success looks like in 6–12 months:

• Arm Hardware engineers can point to real workflow time saved (cycle time, debug time, triage effort) and product quality improvements (number of bugs found, PPA improvements) with credible evidence.
• Prototypes rapidly prove value or stop quickly with clear learning.