• Own the token economics model — cost per million tokens by model, silicon, and traffic profile — and the evaluation and observability that keep it honest, with quality, latency, throughput, and cost measured continuously rather than benchmarked once. Finance, pricing, and sales will rely on this.

• Make the work land commercially. Feed product and go-to-market with what the token factory can actually offer, support sales in demanding customer conversations about model fit, performance and cost, provide technical diligence on inference partners, white-label providers and prospective tuck-in or acquire hire targets, and advise on how IP developed elsewhere in the group is best leveraged here.

What We're Looking For

Required Qualifications

• Significant hands-on experience with modern AI models,
• Depth in inference rather than training alone,
• Working fluency in one or more accelerator ecosystem, and
• Hands-on depth at the compiler, kernel, or runtime layer (CUDA, Triton, ROCm/HIP, XLA, or similar).
• Working fluency in more than one silicon ecosystem. CUDA plus ROCm, Cerebras, or another accelerator experience combined with a realistic view of what portability actually costs.

Preferred Qualifications

• Fluency with the landscape you would be scanning: the frontier labs and open-weight model providers, the serving and inference startups, the silicon vendors, and the research groups doing the work that lasts — and a view on which of them matter. Expect to be asked what you think is currently overhyped, and why.

• Demonstrated ability to do research in the applied sense: taking an open question, investigating it from primary sources — papers, model cards, vendor roadmaps, your own benchmarking — and producing a defensible position under genuine uncertainty. A PhD in a relevant field is one good route to this and is common among people with real depth here; sustained industry research, open-source contribution at depth, or a body of internal technical assessments that changed real decisions are equally valid. Either way, the role turns on the second half: translating that work for engineering, product, go-to-market, and finance, because it informs all four.

• Significant experience working with AI, machine learning, or AI model technologies.

• Strong understanding of AI model architectures and how models are developed.

• Ability to understand both the research and engineering implications of emerging AI technologies.

• Experience working with one or more major model types, such as language, vision, audio, or multimodal models.

• Hands-on depth in inference rather than only training — serving, optimization, and the practical work of getting latency and cost down without giving up quality.

• Working knowledge of a modern serving stack (vLLM, SGLang, TensorRT-LLM, or equivalent) and of quantization, batching, and KV-cache techniques in production.

• Ability to reason quantitatively about cost to serve, and to build models of it that hold up to scrutiny from finance and commercial teams.
• Strong foundational knowledge that allows you to quickly understand unfamiliar model architectures and research.

• Ability to read technical papers and translate research concepts into practical engineering implications.

• Strong analytical, communication, and influencing skills.

• Ability to operate across engineering, research, and infrastructure organizations.

• A track record of collaborating with researchers and engineers across groups and levels to shape long-term research directions and move research into practice.

• Comfortable operating as an individual contributor with high ownership in a lean, early-stage team.

Nice to Have Qualifications

• Depth in a specific kernel or runtime domain beyond general familiarity — attention kernels, collective libraries, memory allocators, or graph compilers.

• Background at a hyperscaler, neocloud, or AI lab operating inference infrastructure at production scale.

• Familiarity with distributed training frameworks (e.g., PyTorch Distributed, DeepSpeed, Megatron). Useful context, though training is not a target-state focus for us.

• Experience with agentic frameworks, tool-use protocols, or multi-agent orchestration.

• Publications, patents, or a recognized external presence in the AI research or systems community.

• Spent your education and career working deeply in AI, but you have the foundational knowledge and intellectual curiosity to quickly understand new architectures and technologies as they emerge.

Location

• Hybrid role based in downtown Bellevue, WA.
• Approximately three days per week in the office.
• Candidates elsewhere in the U.S. who are open to relocation are encouraged to apply.
• U.S. work authorization is required. Visa sponsorship is not currently available.
• Export control: this role involves technologies subject to U.S. export control regulations. Candidate eligibility may be subject to export control screening and, where applicable, licensing.
• Travel: Willingness and ability to travel as needed internationally to data centers and co-locations (up to 25%)

Why Join?

• High-impact technical role: Directly influence the technology direction of an organization building AI infrastructure at scale.
• Ground-floor opportunity: Help establish technical strategy, architecture, processes, and culture within a growing organization.
• High ownership: Operate as a senior individual contributor with substantial autonomy and direct access to senior technical leadership.
• Cross-disciplinary exposure: Work across AI models, inference, accelerators, software systems, networking, infrastructure, and economics.
• Cutting-edge technical problems: Work on multi-accelerator inference, intelligent routing, performance optimization, token economics, and compiler, kernel, and runtime technologies.
• Research with practical impact: Turn emerging research and technology developments into decisions that directly affect engineering, product, commercial strategy, and investment.
• Lean, senior environment: Work with a small group of highly experienced technical contributors rather than within a large management hierarchy.