Senior Staff ML Engineer, Graphcore
More information about location
2+ days a week in office
Advanced AI systems and services
Be an early applicant
Advanced AI systems and services
501-1000 employees
Be an early applicant
More information about location
2+ days a week in office
501-1000 employees
Company mission
To make machine learning faster, easier and more intelligent.
Role
Who you are
- Applicants for this role should have strong experience working with machine learning systems and frameworks, along with a solid understanding of core AI concepts and model behaviour
- Experience working in Machine Learning or ML-adjacent engineering roles
- Strong foundation in core AI and ML concepts (e.g. neural networks, training vs inference, numerical precision, performance trade-offs)
- Hands-on experience with one or more major ML frameworks such as PyTorch, TensorFlow, JAX, or similar
- Strong proficiency in Python for ML workflows, experimentation, and automation
- Experience designing, running, and analysing ML benchmarks or experiments
- Experience working in Linux environments
- Strong analytical and debugging skills, with the ability to reason about model behaviour and system performance
- Bachelor/Master's/PhD or equivalent experience in Computer Science, Maths, Machine Learning, Data Science, or related field
Desirable
- Experience with MLOps pipelines, model deployment, or production ML systems
- Familiarity with performance analysis, profiling tools, or numerical accuracy validation
- Exposure to distributed training or inference systems
- Experience with hardware-accelerated ML, compilers, or system-level performance considerations
- Familiarity with CI/CD systems used for ML workflows
- Experience contributing to open-source ML frameworks or tooling
What the job involves
- The role centres on testing, validating, and benchmarking a complex ML software stack, with a particular focus on performance, reliability, and correctness across modern AI workloads
- Benchmark ML models and frameworks, analysing results to identify regressions, performance bottlenecks, and correctness issues
- Work hands-on with industry-standard ML frameworks to validate functionality and performance across different execution environments
- Build and maintain automated testing and benchmarking pipelines targeting simulators, emulators, and physical hardware
- Collaborate closely with software teams to ensure adequate test coverage for new and existing features
- Develop tooling and scripts (primarily in Python) to support testing, benchmarking, and functional reporting
- Take ownership over aspects of our testing and infrastructure, owning the roadmap and driving innovation independently
- The ML QA team is composed of highly skilled software engineers with a strong focus on automation, software quality, and data-driven validation
- The team works closely with industry-standard machine learning frameworks and models, contributing to upstream open-source projects and collaborating across the wider software organization
- Operating in a fast-paced environment, the team plays a critical role in ensuring reliability, performance, and maintainability across the ML software stack, helping to deliver robust and high-quality products to customers
Our take
The hardware required to train AI models comes at a high cost, both financially and environmentally, producing millions of dollars in expenses and hundreds of tons of greenhouse gases. In response, Graphcore develops accelerator chips designed to expedite AI system training while significantly reducing power consumption.
AI accelerators like those crafted by Graphcore specialise in enhancing specific AI applications, such as artificial neural networks, deep learning, and machine learning. Utilising multicore designs and prioritising low-precision arithmetic or in-memory computing, these accelerators elevate the performance of large AI algorithms, achieving breakthrough results in natural language processing and computer vision.
Graphcore has ambitious plans to create an AI supercomputer, aiming to be one of the most powerful in its class. However, progress on this has extended beyond its planned 2024 debut, and the company was purchased by Softbank later that year. Though the acquisition by the Japanese multinational lowered Graphcore's valuation and highlighted structural problems in the local financing of UK tech pioneers, the buyout will undoubtedly aid the company in its ambitions to shape the future of AI computing.
Freddie
Company Specialist at Welcome to the Jungle
Insights
Company
Funding (last 2 of 6 rounds)
Dec 2020
$222m
SERIES E
Feb 2020
$150m
SERIES D
Total funding: $682m
Company benefits
- Work-Life Balance: We believe you should have a good work-life balance. Everyone's situation is different, so we offer flexible working arrangements and start and finish times, allowing you to choose when to work from home or come into the office
- Wellbeing: We want to ensure that our employees have the resources and support they need to prioritise their health and wellbeing
- Healthcare: We offer Private Medical Insurance (PMI) to our employees to help them take care of their health and that of their loved ones
- Progression: We prioritise learning and development as an integral part of our culture at Graphcore. Our Talent Development team is dedicated to equipping all our employees with the necessary tools, resources, and support to enhance their skills and reach their full potential in their careers
- Pension: We believe that our employees deserve the best when it comes to planning for their future
- Income Protection: Graphcore understands that unforeseen circumstances can happen, and we want to ensure our employees have the necessary financial security in place
Company HQ
Old City, Bristol, UK
Leadership
Nigel Toon
(Co-Founder & Exec Chair)Previously the President and CEO at picoChip Designs (processors for telecoms). As well as the VP and GM of Europe at Altera (semiconductors, merged with Intel).
Simon Knowles
(Co-Founder & CTO)Founded two successful semiconductor companies, Element14 and Icera, for a combined value of over $1 billion.
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