Applied AI Engineer, Etched
Kernel Performance
$150-225k
5 days a week in office (San Jose, CA)
Designing specialised chips for language model inference
Be an early applicant
Designing specialised chips for language model inference
21-100 employees
Be an early applicant
$150-225k
5 days a week in office (San Jose, CA)
21-100 employees
Company mission
To create the world’s most powerful servers for transformer inference.
Role
Who you are
- 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
- Strong candidates may also have experience with:
- 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
What the job involves
- Every model release presents a new opportunity to push the frontier on kernel engineering
- Future performance breakthroughs will come from AI systems that can understand model architectures and hardware, run thousands of experiments, learn from compiler and profiler feedback, and discover the most performant implementations faster than the best engineers
- You will build that system
- Your mandate is to build AI systems that autonomously turn newly released model architectures into correct, production-ready implementations optimized for Etched hardware
- These systems should explore broader design spaces, learn from every experiment, and reach peak performance faster than any traditional kernel-development workflows
- Etched offers a uniquely tight research loop: proprietary hardware, compiler, runtime, kernels, production workloads, and dedicated in-office compute under one roof
- You will teach models using proprietary performance signals, iterate on their proposals, and make every experiment improve both the performance optimization system and the hardware it runs on
- Own the system that turns new model architectures into verified, production-ready kernels and model mappings
- Build agents that understand Etched hardware, design experiments, generate implementations, compile and profile them, diagnose bottlenecks, and iterate with our teams, to the limits of model autonomy
- Design evals covering correctness, numerical stability, latency and efficiency
- Turn profiler traces, simulation, hardware counters, and expert judgment into structured signals models can learn from
- Curate proprietary datasets and optimization memory from complete trajectories, expert demonstrations, counterexamples, and production outcomes
- Build fast, reproducible experiment infrastructure and observability so experiments remain interpretable, trustworthy, and high-throughput
- Ship model-generated improvements to production and quantify their impact on end-to-end system performance
- Partner deeply with other architecture teams to shape new abstractions and Etched’s hardware-software roadmap
- Continuously evaluate new model releases and deploy the best for each stage of the optimization loop
Our take
With AI developing rapidly, the chips that power them are doing so too. The computing requirements of technologies such as Chat GPT will increasingly require customised, hard-wired chips called ASICS, designed specially for the execution of AI models. Whilst GPUs are currently used for AI computing, Etched is making a bet on AI-hardwired chips becoming the predominant powering force of AI.
It’s a gamble, the founders themselves know that if transformers become obsolete, so will their AI-powering product. The SOHU chip that Etched builds differs from GPUs in that it has the AI architecture etched into the chip itself. General Processing Units are “general” and costly, so Etched is betting on specialised transformers taking their place in the near future.
Investors are drawn to Etched’s boldness. The 2022-founded company achieved $120 million in Series A funding just two years into its life, and received a further $500 million at the start of 2026. Etched plans to use this to amp up its competition with GPU company Nvidia, and claims to be 20 times faster than the latter’s AI-powered general processor - and significantly less costly too. Time will tell if Etched’s exciting and potentially revolutionary bet on tailored AI chips and transformers pays off.
Steph
Company Specialist at Welcome to the Jungle
Company
Funding (last 2 of 3 rounds)
Jan 2026
$500m
LATE VC
Jun 2024
$120m
SERIES A
Total funding: $625.4m
Company benefits
- Competitive Salary and Equity - We offer a competitive salary and equity package that aligns with industry standards
- Benefits - Your wellbeing is important to us. We offer unlimited PTO, life insurance, and a 401(k)
- Full Insurance Coverage - Our insurance includes full medical, dental, and vision with 100% of premium covered
Company HQ
Horace Mann and SJSU, San Jose, CA
Leadership
Robert Wachen
(Co-Founder & President)Also the Co-Founder of Prod and Mentor Labs, Birthday Cakes 4 Free Maryland and Generate Sales Online.
Gavin Uberti
(Co-Founder & CEO)Previously Software Engineer at OctoML, Algorithms and BAckend Engineer at Coursedog and was a Software Engineer Intern at Xnor.ai.
Chris Zhu
(Co-Founder)Was a Teaching Fellow at Harvard and a Software Engineering Intern at Amazon and AvantStay. Was an Analyst at Harvard Undergraduate Capital Partners.
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