---
title: 'Senior Machine Learning Engineer (Large Systems) at Graphcore'
canonical: 'https://feeny.ai/job/senior-machine-learning-engineer-large-systems-graphcore-bristol-1asw2kjcwsx0'
type: 'job'
last_seen: '2026-09-08'
---

# Senior Machine Learning Engineer (Large Systems) at Graphcore

- **Company:** Graphcore
- **Location:** Bristol, United Kingdom
- **Posted:** 2025-12-02
- **Last confirmed live:** 2026-09-08
- **Apply:** https://job-boards.greenhouse.io/graphcore/jobs/8315158002

## 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 Senior Machine Learning Engineer in the Applied AI team at Graphcore, you will contribute to advancing AI technology by developing and optimising AI models tailored to our specialised hardware. You will work on large scale systems where performance is critical to the success of our projects. Working closely with the Software development and Research teams, you will play a critical role in identifying opportunities to innovate and differentiate Graphcore’s technology. We seek engineers with strong technical skills and an understanding of AI model implementation at scale, eager to make a tangible impact in this rapidly evolving field.

The Team

The Applied AI team’s role is to be proxies for our customers, we need to understand the latest AI models, applications, and software to ensure that Graphcore’s technology works seamlessly with the AI ecosystem and at scale. We build reference applications, contribute to key software libraries e.g. optimising kernels for efficiency on our hardware, and collaborate with the Research team to develop and publish novel ideas in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications.

If you're excited about advancing the next generation of AI models on cutting-edge hardware, we’d love to hear from you!

## Responsibilities and Duties

- Implement latest machine learning models and optimise them for performance and accuracy, scaling to 1000s of accelerators.
- Test and evaluate new internal software releases, provide feedback to software engineering teams, make necessary code fixes, and conduct code reviews.
- Benchmark models and key ML techniques to identify performance bottlenecks and improve model efficiency.
- Design and conduct experiments on novel AI methods, implement them and evaluate results.
- Collaborate with Research, Software, and Product teams to define, build, and test Graphcore’s next generation of AI hardware.
- Engage with AI community and keep in touch with the latest developments in AI.

Candidate Profile

Essential:

- Bachelor/Master's/PhD or equivalent experience in Machine Learning, Computer Science, Maths, Data Science, or related field.
- Proficiency in deep learning frameworks like PyTorch/JAX.
- Strong Python or C++ software development skills
- Expertise in deep learning from model training to optimisation and evaluation.
- Capable of designing, executing and reporting from ML experiments.
- Developed deep understanding of performance bottlenecks and how to overcome them.
- Ability to move quickly in a dynamic environment
- Enjoy cross-functional work collaborating with other teams.
- Strong communicator - able to explain complex technical concepts to different audiences.

Desirable:

- Experience in one or more of:
- MLOps for Kubernetes-based clusters
- Building production systems with large language models
- Efficient computing based on low-precision arithmetic.
- Experience writing C++/Triton/CUDA kernels for performance optimisation of ML models.
- Experience in distributed training or inference of ML models across 64+ accelerators.
- Familiarity with HPC systems and networking including Infiniband, NVLink, RoCE technologies.
- Have contributed to open-source projects or published research papers in relevant fields.
- Knowledge of cloud computing platforms.
- Keen to present, publish and deliver talks in the AI community.

## 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. [builtin.com/company/graphcore](https://builtin.com/company/graphcore)

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