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Designworks Talent LLC
Designworks Talent LLC
B2BHR TechRecruitment
Designworks Talent LLC is a recruitment and talent advisory firm serving high-growth startups and global enterprises. Founded in 2009, the company combines experienced recruiters with AI-enabled tools to support faster, more targeted hiring. Its work spans full-service searches, project-based recruiting, and flexible on-demand models, alongside scalable hiring strategy, recruiting operations, enterprise talent acquisition leadership, data-informed sourcing, and candidate experience. The team’s approach is designed to give organizations adaptable support as their hiring needs evolve.
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Applied AI Researcher – Infrastructure and Inference

Lead applied research on AI infrastructure, inference systems, and accelerator strategy. Shape engineering, product, finance, and commercial decisions for scalable AI workloads.

USUnited StatesWAWashingtonHybridFull-timeMiddleSeniorEnglish RequiredAI Research ScientistMachine Learning Engineer

Description

  • Define the company’s long-term direction for data center infrastructure
  • Monitor vendor and hyperscaler roadmaps, research, standards activity, startup and venture developments across power, cooling, construction, rack and hall design, siting, regulation, and infrastructure economics
  • Develop and validate product and engineering positions on what to build, purchase, or pursue through partnerships
  • Evaluate technology positions against cost, schedule, and physical infrastructure limitations
  • Maintain the technical reference model for client halls across GPU generations, covering density, power envelopes, cooling topologies, and site capacity
  • Visit operating and potential data center sites
  • Shape cost models for finance, pricing, and sales, including cost per rack, megawatt, and GPU-hour
  • Support sales and delivery teams in technically complex customer and partner discussions
  • Advise product and go-to-market teams on credible capabilities and delivery timelines
  • Perform technical diligence on infrastructure partners, colocation providers, vendor reference designs, and potential tuck-in acquisitions
  • Validate power strategies covering interconnect availability, queue position, utility and PPA structures, on-site generation, long-lead equipment, site selection, and construction sequencing
  • Partner with engineering, product, infrastructure, finance, and commercial teams as a senior individual contributor

Requirements

  • Substantial hands-on experience with modern AI models
  • Strong expertise in inference, beyond model training alone
  • Working fluency with one or more accelerator ecosystems
  • Hands-on experience at the compiler, kernel, or runtime level, using technologies such as CUDA, Triton, ROCm/HIP, XLA, or similar
  • Working fluency across multiple silicon ecosystems
  • Extensive experience with AI, machine learning, or AI model technologies
  • Strong understanding of AI model architectures and development processes
  • Ability to assess the research and engineering implications of emerging AI technologies
  • Experience with one or more major model categories, including language, vision, audio, or multimodal models
  • Hands-on inference experience covering serving, optimization, and latency and cost reduction while maintaining quality
  • Working knowledge of a modern serving stack such as vLLM, SGLang, TensorRT-LLM, or an equivalent
  • Production experience with quantization, batching, and KV-cache techniques
  • Ability to analyze serving costs quantitatively and build models that withstand finance and commercial review
  • U.S. work authorization is required
  • Visa sponsorship is not currently available
  • Eligibility may require U.S. export-control screening and, where applicable, licensing
  • Willingness and ability to travel internationally as needed, up to 25%
  • Preferred: familiarity with frontier labs, open-weight model providers, serving and inference startups, silicon vendors, and relevant research groups
  • Preferred: applied research experience using primary sources, papers, model cards, vendor roadmaps, or benchmarks to develop defensible positions under uncertainty
  • Preferred: ability to communicate research findings to engineering, product, go-to-market, and finance teams
  • Preferred: experience spanning multiple data center builds and vendor reference designs
  • Preferred: experience with liquid cooling or high-density rack deployments above 100 kW
  • Preferred: experience commissioning, planning capacity for, or handing over new data center halls
  • Preferred: publications, patents, standards-body participation, or a visible external infrastructure presence
  • A PhD in a relevant field is one possible path but is not required

Benefits

  • Approximately three days per week working from the office
  • International travel to data centers and colocation facilities may be required, up to 25%
  • High-impact technical work with direct influence over AI infrastructure strategy
  • Substantial ownership and autonomy with direct access to senior technical leadership
  • Cross-functional exposure spanning AI models, inference, accelerators, software systems, networking, infrastructure, and economics
  • Advanced technical challenges involving multi-accelerator inference, intelligent routing, performance optimization, token economics, and compiler, kernel, and runtime technologies
  • Research with direct applications to engineering, product, commercial strategy, and investment decisions
  • Lean, senior environment with experienced technical contributors

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