Graphcore

Artificial Intelligence

GPUArchitect

$450–750k ~AI est. Milpitas, California, United States
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“GPU Architect at Graphcore. Skills: GPU Architecture, AI Accelerators, Parallel Processing, Hardware Efficiency. Define next generation AI accelerators. Define multi-GPU cluster architecture”

Industry & Context.

Artificial Intelligence
Problems you'll solve

Identify bottlenecks; Guide architectural trade-offs; Characterize complex AI mathematical operations

What They're Looking For.

Must Have

10+ years GPUs experience, 10+ years AI accelerators experience, 10+ years parallel computer systems experience, Microarchitecture expertise, SIMD/SIMT execution models knowledge, Instruction scheduling knowledge, Hardware acceleration for ML algorithms knowledge, Advanced manufacturing techniques knowledge, Rack level L11 liquid cooled solutions knowledge, RDMA environments data pathways experience, Hardware clustering protocols experience, C++ proficiency, Python proficiency, AI mathematical operations characterization ability, BS or MS or equivalent experience

Nice to Have

Rack-scale GPU designs qualification experience, NPI manufacturing experience, Testing experience, Quality calculations experience, Reliability calculations experience

What You'll Do.

Define next generation AI accelerators

Define multi-GPU cluster architecture

Lead technology characterization

Lead reliability strategies

Lead interconnect performance strategies

Collaborate across hardware teams

Collaborate across firmware teams

Collaborate across AI silicon teams

Build GPU infrastructure

Accelerate ML frameworks

Accelerate localized inference engines

Build and analyze simulators

Build and analyze analytical models

Forecast workload performance

Guide architectural trade-offs

Influence silicon architecture roadmaps

Mentor engineering teams

Drive engineering standards

Understand lifecycle bath-tub curves

Understand repair rates

Understand uptime curves

Detect inadequate manufacturing frameworks

Correct inadequate manufacturing frameworks

Characterize data pathways

How You'll Work.

Team & Collaboration

Hardware teams; Firmware teams; AI silicon teams

Full Job Description

About us Graphcore is one of the world’s leading innovators in Artificial Intelligence compute. It is developing hardware, software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry. As part of the SoftBank Group, Graphcore is a member of an elite family of companies responsible for some of the world’s most transformative technologies. We are opening a new AI Engineering Campus in Austin, which will play a central role in Graphcore's work building the future of AI computing!. Graphcore’s teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore enjoys a culture of continuous learning and constant innovation. Job Summary We are seeking a highly accomplished experienced GPU Architect to define the next generation of AI accelerators and multi-GPU cluster architecture. As the demand for trillion-parameter LLM training and high-throughput localized inference accelerates, the role of GPU architecture has never been more critical. In this role, you will lead the technology characterization, reliability, and interconnect performance strategies that ensure our compute fabrics scale flawlessly. You will collaborate deeply across hardware, firmware, and AI silicon teams to build GPU infrastructure capable of pushing the absolute limits of parallel processing and hardware efficiency. Responsibilities and Duties Hardware-Software Co-Design: Collaborate with software engineering to ensure the AI compute and Rack level hardware architectures fundamentally accelerate lower-level ML frameworks and localized inference engines (e.g., vLLM, Ollama, TensorRT). Performance Modeling: Build and analyze cycle-accurate simulators and analytical models to identify bottlenecks, forecast workload performance, and guide architectural trade-offs.

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