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Atom Computing is seeking a Principal AI Developer Experience Engineer to shape how their engineering organization leverages AI and LLMs in software development workflows.
You will define and maintain best practices for AI-assisted coding, testing, code review, and research workflows across the company. A key focus is influencing guidelines for using LLMs on performance-critical software—embedded C++ and Rust, RTOS applications, and kernel device drivers—determining where AI assistance is appropriate, how generated code is reviewed and tested, and how memory safety, timing, and determinism are verified before merge.
You'll make forward-looking improvements to the company's AI tooling and integrations to keep pace with advances in models and agents and shifts in engineer tool preferences. You'll track industry benchmarks for AI-assisted developer productivity to ensure practices remain current.
A critical responsibility is building measurement systems for AI and LLM-driven workflows: tracking defect and revert rates of AI-assisted changes, review findings, test coverage, performance regressions, new capabilities added, and time saved. You'll use these results to refine tools and guidelines iteratively.
You'll also ensure safe and reliable operation of AI in quantum processors, a unique domain requirement reflecting the company's focus.
This is a high-influence role requiring you to guide a multi-disciplinary engineering organization (50+ engineers) on AI adoption, communicate clearly with physicists and new hires alike, and establish organizational standards for responsible AI use in critical systems.
REQUIREMENTS:
- Master's or PhD in Computer Science, Software Engineering, or Artificial Intelligence
- 8+ years of software development
- Strong software engineering skills in Python
- Experience writing embedded software in C++ or Rust, including manual memory management, hard real-time constraints, and kernel device driver implementation
- Demonstrated experience influencing an organization of 50+ engineers using AI agents (Claude Code, Cursor, or GitHub Copilot) on a daily basis, with ability to identify pitfalls and measure impact
- Working knowledge of how LLMs behave in practice: context management, prompting, agentic workflows, harnesses, effective safety practices, tool use, and judging output quality
- Clear written and spoken communication with audiences ranging from physicists to new software hires; ability to influence a multi-discipline engineering organization
