Graphcore
AI Compute
SeniorMachineLearningEngineer(LargeSystems)
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Machine Learning Engineer (Large Systems) at Graphcore. Skills: Machine Learning, AI, Python, distributed training. Implement machine learning models. optimise models for performance”
Industry & Context.
solve the toughest problems; performance bottlenecks
right to work in the UK
What They're Looking For.
Must Have
Machine Learning, Computer Science, Maths, Data Science, deep learning frameworks, PyTorch, JAX, Python, C++ software development, deep learning, model training, optimisation, evaluation, distributed training, inference of ML models, 64+ accelerators, designing ML experiments, executing ML experiments, reporting from ML experiments, performance bottlenecks, move quickly, dynamic environment, cross-functional work, collaborating with other teams, communicator
Nice to Have
MLOps for Kubernetes-based clusters, production systems with large language models, Efficient computing, low-precision arithmetic, C++ kernels, Triton kernels, CUDA kernels, performance optimisation, HPC systems, networking, Infiniband, NVLink, RoCE technologies, open-source projects, published research papers, cloud computing platforms, present, publish, deliver talks
What You'll Do.
Implement machine learning models
optimise models for performance
optimise models for accuracy
scale models to 1000s of accelerators
Test new software releases
evaluate new software releases
provide feedback to software engineering
identify performance bottlenecks
improve model efficiency
Design AI experiments
conduct AI experiments
implement novel AI methods
evaluate AI experiment results
build Graphcore's AI hardware
test Graphcore's AI hardware
Engage with AI community
keep in touch with AI developments
How You'll Work.
Team & Collaboration
Working closely with Software development; Working closely with Research teams; Collaborate with Research teams; Collaborate with Software teams; Collaborate with Product teams; cross-functional work; collaborating with other teams
Communication Scope
explain complex technical concepts
Full 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 ge
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