Applied Researcher – AI Expert

Location: Hybrid | Bellevue, WA (downtown)

About the Opportunity

Our client is seeking an experienced AI Expert / Applied Researcher to help shape how a fast-growing technology organization understands and applies the rapidly evolving landscape of AI models, architectures, inference technologies, and accelerator systems.

This role sits at the intersection of AI research, systems engineering, and infrastructure strategy. You will evaluate emerging technologies, translate research into practical engineering implications, and help guide decisions around AI infrastructure, inference optimization, model serving, accelerator platforms, and the economics of delivering AI workloads at scale.

This is a highly strategic individual contributor role with broad technical influence. You will work closely with engineering, product, infrastructure, finance, and commercial teams to help determine what technologies to build, adopt, partner for, or invest in.

 

What You'll Do

• Hold the company's view of where AI is going. Track lab and academic research, model releases, open-source projects, vendor roadmaps, and the startup and venture landscape across the model landscape, inference optimization, serving and routing, the kernel and compiler layer, post-training and adaptation, the agentic layer, evaluation, and the security and sovereignty constraints around all of it. Right now that means questions like what low-precision formats really cost in output quality, whether sparse attention solves long-context economics, how far open-weight models displace frontier APIs for serving volume, and what agent traffic does to caching and scheduling. Those specific questions will have changed within a quarter — holding the current version of them is the job.

• Formulate and validate the product and engineering thesis. Turn that view into a defensible position on what we build, buy, or partner for across routing, serving, and the kernel and runtime layer, pressure-tested against measured cost per token and output quality — and say so plainly when the evidence does not hold up.

• Own the company's technical position on inference: optimization across quantization, speculative decoding, KV-cache management, batching, prefill/decode disaggregation and long-context serving; the serving stack and what we adopt, extend, or build ourselves; and the intelligent routing logic that decides which model and which silicon serves each request.

• Own multi-silicon portability: what it actually takes to run the same model well across NVIDIA, AMD and Cerebras, and where the compiler, kernel, and runtime layer is worth building versus buying or partnering for.