Applied AI Engineer (Silicon Engineering)

Join our team as an Applied AI Engineer, where you'll work closely with our hardware teams to build AI agents and tooling that enhance chip development. This internal role focuses on applying AI to the chip-building process, rather than customer-facing applications. You'll be responsible for integrating AI agents into our internal infrastructure, championing adoption, and measuring success based on the speed of the chip team's progress.

• Concevoir et mettre en œuvre des agents et des outils qui multiplient la productivité des équipes matérielles, en intégrant des agents LLM dans les flux de simulation, de régression et d'analyse.
• Identifier les points de douleur les plus importants des équipes matérielles et transformer ces problèmes en flux de travail automatisés avec une adoption mesurable.
• Construire, déployer et maintenir des flux de travail d'agents LLM qui accélèrent le développement des puces, y compris le triage des débogages, le travail sur les bancs d'essai et la génération de scripts EDA.
• You do not need to be a chip designer or a traditional software engineer — you need to be an exceptional problem solver who has shipped real agentic systems, works comfortably across stacks and domains, and uses AI to ramp on hard new problems fast
• High agency and comfort with ambiguity — you can find the problem, not just solve the stated one
• A track record of solving hard problems across stacks and domains — you enjoy being dropped into unfamiliar territory and figuring it out
• Hands-on experience building and shipping LLM-based agents or AI tooling that real users depend on (beyond calling an API — context engineering, tool integration, orchestration, failure analysis)
• Interest in chip development and the ability to ramp quickly on a deeply technical domain. Hardware experience is a real plus, but not required — you will be willing and able to learn quickly
• Comfort with Python and code: you can read it, modify it, debug it, and direct AI to write it well. We do not care whether you write code from scratch — we care whether you ship things that work
• An eval-driven mindset: you measure whether AI systems actually work before scaling them
• Fluency using AI to learn and ramp on new problems — agentic coding tools, deep research, and frontier models are how you work, not an add-on
• Chip development in any form (the strongest plus): RTL/SystemVerilog, functional verification (UVM), DFT, physical design/STA, FPGA, emulation, or silicon bring-up and validation
• EDA tool flows and Tcl scripting; reading waveforms, logs, and regressions
• Fine-tuning or post-training (SFT, RLHF/DPO), RAG over proprietary technical data, or multi-agent orchestration
• Deep software engineering: C++ or Rust, developer-facing internal platforms, CI/CD at scale, or infrastructure (Docker, Slurm, Ray)