--- title: 'AI Research Engineer at Graphcore' canonical: 'https://feeny.ai/job/ai-research-engineer-graphcore-cambridge-9d765f03570y' type: 'job' last_seen: '2026-09-08' --- # AI Research Engineer at Graphcore - **Company:** Graphcore - **Location:** Cambridge, MA - **Posted:** 2026-07-14 - **Last confirmed live:** 2026-09-08 - **Apply:** https://job-boards.greenhouse.io/graphcore/jobs/8632583002 ## Job description ## About Graphcore At Graphcore, we’re building the future of AI compute.We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence. Job Summary As a research engineer at Graphcore, you will contribute to the advancement of AI research, investigating new ideas that push the limits on important AI/ML problems. Specialised hardware has been the key driver of the progress of AI over the last decade, and we believe that hardware-aware AI algorithms and AI-aware hardware developments will continue to be critical to advancing this exciting field. We are therefore looking for individuals who combine strong machine learning experience with practical engineering skills to deliver impactful AI research. We are seeking AI researchers with strong software engineering experience, particularly in lower-level programming and performance optimisation for hardware efficiency. Our research spans a broad range of topics, including efficient training and inference, world models, life sciences, reinforcement learning, and beyond. You will work closely with researchers to generate ideas and translate them into scalable implementations, contributing to publications and projects that help to steer the future of AI hardware. The Team Graphcore Research participates in both fundamental and applied research, to characterise the computational requirements of machine intelligence and to demonstrate how hardware can drive the next generation of innovative AI models. We publish at leading AI/ML conferences (NeurIPS, ICML, ICLR) as well as specialist workshops, and collaborate with other research teams and organisations across the world. We pride ourselves on being a supportive and collaborative team, where we organise around our individual research interests to solve problems together in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications, including for sequence- and graph-based data. We’re based across London, Cambridge and Bristol, with projects and discussions that involve all our locations. Perhaps the best way to get an idea of what we’re all about is to read one of our [papers](https://graphcore-research.github.io/publications/) or an article on our [blog](https://graphcore-research.github.io/posts/). If you’re excited to work at the cutting edge of AI supported by new hardware and want to develop your skills in this area, we’d love to hear from you! ## Responsibilities and Duties - Generate AI/ML ideas, design experiments, implement them & evaluate results. - Prepare, submit & present your work to AI conferences and workshops. - Provide technical insight to internal teams by designing experiments and delivering clear, actionable reports. - Collaborate with researchers, silicon and software engineers at Graphcore to help define, build and test Graphcore’s next generation of AI hardware. About you: Essential: - Master’s, PhD or equivalent experience in a technical discipline (e.g., Maths, Statistics, Computer Science, Physics, Chemistry). - Python programming in a modern deep learning framework, e.g. PyTorch or JAX. - Familiar with deep learning fundamentals: models, optimisation, evaluation and scaling. - Capable of designing, executing and reporting from ML experiments. - Lower-level programming for hardware efficiency, e.g. C++/CUDA/Triton. - Practical familiarity with hardware capabilities for deep learning – threads, caches, vector & matrix engines, data dependencies, bus widths and throttling. - Practical familiarity with software stacks for deep learning – compilation, kernel fusion, XLA/ATen ops, streams, and asynchronous execution Desirable: - Mathematics skills to support the above: calculus, probability theory and linear algebra. - Experience submitting papers to international scientific conferences or workshops. ## Benefits In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments. Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications ## About Graphcore ## Company Overview - **One-liner**: Graphcore designs and builds the Intelligence Processing Unit (IPU), a processor purpose-built for machine intelligence, and provides a complete AI compute stack from silicon to software. - **Entity Type**: Private (wholly owned subsidiary of SoftBank Group Corp as of 2025/2026) - **Headquarters**: Bristol, England, UK - **Founded**: 2016 - **Founders**: Nigel Toon and Simon Knowles ## Core Business - **Primary industry**: AI hardware/accelerators, semiconductor design, AI software platform - **Target customers**: Cloud providers (Microsoft Azure), enterprises, AI researchers, hyperscalers - **Mission statement**: “Let innovators create the next breakthroughs in machine intelligence to enhance human potential” ## Products & Services - **[IPU (Intelligence Processing Unit)](https://www.graphcore.ai/)**: A processor designed from the ground up for machine intelligence workloads; uses a massively parallel architecture (MIMD) with fine-grained compute and on-chip memory. - **[IPU-Machine M2000](https://www.graphcore.ai/about)**: Second-generation IPU platform – a 1U server appliance housing 4 MK2 GC200 IPU processors, delivering an 8× performance uplift over the first generation. - **[C2 IPU Accelerator PCIe Card](https://www.graphcore.ai/about)**: PCIe form factor accelerator for server integration, first demonstrated at ICML 2018. - **[Poplar Software Stack](https://www.graphcore.ai/)**: Full SDK and framework integration (PyTorch, TensorFlow) that lets developers target the IPU directly. ## Market Standing - **Valuation/Market Cap**: Acquired by SoftBank Group Corp (amount undisclosed); prior to acquisition valued at $2.77 B (Series E, lead by Ontario Teachers’ Pension Plan Board). - **Key Metric**: Total funding raised prior to acquisition – over $680 M across Series A through E. - **Notable Investors/Partners**: SoftBank (owner), Microsoft (customer and investor), BMW, Samsung, Dell Technologies, Bosch, Sequoia Capital, Atomico, Fidelity, Baillie Gifford, Ontario Teachers’. - **Growth Signals**: Part of the fast-growing SoftBank AI ecosystem; expanding teams globally (Bristol, London, Cambridge, Gdańsk, Hsinchu, Bengaluru); first customer (Microsoft Azure) live since 2018. ## Competitive Advantages - **First purpose-built AI processor**: The IPU architecture is fundamentally different from GPUs – designed specifically for the sparse, parallel nature of neural network computation. - **Full stack ownership**: Graphcore controls silicon, system hardware, and software (Poplar), enabling tight optimisation across the entire compute chain. - **Cloud + on‑prem availability**: IPUs are available in the cloud on Microsoft Azure and as hardware from Dell, giving customers flexible deployment options. - **Strong IP and ecosystem**: 99+ engineering staff; deep expertise in semiconductor, systems, and AI software, backed by long-term SoftBank investment. ## Strategic Focus - **Scale AI infrastructure**: Delivering key technology into SoftBank’s global AI ecosystem and expanding the IPU platform to meet growing demand for AI compute. - **Expand into new regions**: Opening offices and hiring in India (Bengaluru) and across Europe (Gdańsk, London, Cambridge) to accelerate R&D and customer support. - **Democratise AI**: Continuing to lower the barrier for machine intelligence by providing accessible, high-efficiency hardware and software for innovators worldwide. ## Why Work Here - **Flexible hybrid work**: Employees are expected on-site three days a week, with trust and flexibility to balance work and life. - **Comprehensive benefits**: Unlimited annual leave, private medical insurance (UK), dental & health cash plan, income protection, life assurance, pension matched up to 5%, generous parental leave, and a personal development plan. - **Culture and growth**: ML reading groups, board game socials, book clubs, yoga, running, watersports, team events; strong DE+I initiatives; ambitious but supportive environment. - **Perks**: Free barista‑made coffee, snacks, and wellbeing rooms; cycle‑to‑work scheme; gym membership discount; education/training allowances. - **Purpose-driven work**: Opportunity to shape the future of AI compute and see your contributions used by global innovators. ## Sources 1. [graphcore.ai/about](https://www.graphcore.ai/about) 2. [graphcore.ai/](https://www.graphcore.ai/) 3. [graphcore.ai/careers](https://www.graphcore.ai/careers) 4. [graphcore.ai/jobs](https://www.graphcore.ai/jobs) 5. 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