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Applied AI Engineer (Kernel Performance)

Etched · San Jose, United States

External listingfull-time8 days ago

About The Role

  • Experience with compiler optimization and performance analysis
  • Familiarity with machine learning frameworks and libraries
  • Knowledge of hardware architecture and design
  • Experience with distributed systems and cloud computing
  • Background in computer science, engineering, or a related field
  • Construire des systèmes d'IA qui transforment de manière autonome de nouvelles architectures de modèles en implémentations correctes et prêtes pour la production.
  • Développer des agents qui comprennent le matériel Etched, conçoivent des expériences, génèrent des implémentations, les compilent et les profilent.
  • Évaluer en continu les nouvelles versions de modèles et déployer les meilleures pour chaque étape du cycle d'optimisation.
  • 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
  • A track record of solving hard problems across stacks and domains — you enjoy being dropped into unfamiliar territory and figuring it out
  • Kernel experience: you've written or tuned kernels and can explain the mechanisms and performance impact of optimizations you’ve shipped
  • Comfort with both Python and low-level 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
  • Moving fluidly between research exploration, agentic experimentation, low-level debugging, and production execution
  • Hands-on experience building and shipping LLM-based agents or AI tooling that real users depend on in production environments (beyond calling an API — context engineering, tool integration, orchestration, failure analysis)
  • First principles thinking on accelerator performance: memory hierarchy, data movement, parallelism, synchronization, and low-precision computation
  • Fine-tuning or post-training, RAG over proprietary data, and/or multi-agent orchestration
  • An eval-driven mindset: you measure whether AI systems work before scaling them
  • High agency and comfort with ambiguity — you find the real problem to solve
  • Strong candidates may also have experience with:

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