Applied AI Engineer, Kernel Performance

A track record of solving hard problems across stacks and domains — you enjoy being dropped into unfamiliar territory and figuring it out

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.

Kernel experience: you've written or tuned kernels and can explain the mechanisms and performance impact of optimizations you’ve shipped

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

Moving fluidly between research exploration, agentic experimentation, low-level debugging, and production execution.

First principles thinking on accelerator performance: memory hierarchy, data movement, parallelism, synchronization, and low-precision computation.

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)

An eval-driven mindset: you measure whether AI systems work before scaling them

Fine-tuning or post-training, RAG over proprietary data, and/or multi-agent orchestration

High agency and comfort with ambiguity — you find the real problem to solve

Etched believes in the Bitter Lesson . We are the first inference-focused frontier AI system, betting early on transformer and transformer-like architectures and on increasing model sizes. Our addressable market is the entirety of inference, unlike many of our competitors.

We are a fully in-person team in San Jose (Santana Row), and greatly value engineering skills. We do not have boundaries between engineering and research, and we expect all of our technical staff to contribute to both and work across disciplines as needed.