Applied Researcher – AI Expert

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.

Responsibilities

Hold the company's view of where AI is going by tracking lab and academic research, model releases, open-source projects, vendor roadmaps, and the startup and venture landscape; Formulate and validate the product and engineering thesis on what to build, buy, or partner for, pressure-tested against measured cost per token and output quality; Own the company's technical position on inference, including optimization across quantization, speculative decoding, KV-cache management, batching, prefill/decode disaggregation, and long-context serving, the serving stack, and intelligent routing logic; Own multi-silicon portability across NVIDIA, AMD and Cerebras, and where the compiler, kernel, and runtime layer is worth building versus buying or partnering for; Own the token economics model (cost per million tokens by model, silicon, and traffic profile) and the evaluation and observability that keep it honest; Make the work land commercially by feeding product and go-to-market, supporting sales in customer conversations, providing technical diligence on inference partners, white-label providers and acquisition targets, and advising on leveraging IP developed elsewhere in the group

Requirements

Significant hands-on experience with modern AI models; Depth in inference rather than training alone; Working fluency in one or more accelerator ecosystem; 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, with CUDA plus ROCm, Cerebras, or another accelerator experience combined with a realistic view of what portability actually costs

Nice to have

Fluency with the frontier labs, open-weight model providers, serving and inference startups, silicon vendors, and research groups; Demonstrated ability to do applied research from primary sources and produce a defensible position under uncertainty (a PhD is one route, but sustained industry research, deep open-source contribution, or internal technical assessments are equally valid); Significant experience with AI, machine learning, or AI model technologies; Strong understanding of AI model architectures and how models are developed; Experience with one or more major model types (language, vision, audio, or multimodal); 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; Ability to read technical papers and translate research into engineering implications; Strong analytical, communication, and influencing skills; Ability to operate across engineering, research, and infrastructure organizations; Track record of collaborating with researchers and engineers to move research into practice; Comfortable operating as an individual contributor with high ownership in a lean, early-stage team; Depth in a specific kernel or runtime domain (attention kernels, collective libraries, memory allocators, graph compilers); Background at a hyperscaler, neocloud, or AI lab operating inference at production scale; Familiarity with distributed training frameworks (PyTorch Distributed, DeepSpeed, Megatron); 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

Benefits

High-impact technical role directly influencing the technology direction of an organization building AI infrastructure at scale; Ground-floor opportunity to help establish technical strategy, architecture, processes, and culture; High ownership as a senior individual contributor with substantial autonomy and direct access to senior technical leadership; Cross-disciplinary exposure across AI models, inference, accelerators, software systems, networking, infrastructure, and economics; Cutting-edge technical problems in multi-accelerator inference, intelligent routing, performance optimization, token economics, and compiler, kernel, and runtime technologies; Research with practical impact on engineering, product, commercial strategy, and investment; Lean, senior environment working with a small group of highly experienced technical contributors