About the company
Graphcore develops specialised hardware and software for AI. The Applied AI team acts as proxies for customers, ensuring Graphcore's technology works seamlessly with the AI ecosystem at scale.
About the role
Help scale state-of-the-art AI models across thousands of accelerators. As a Senior Machine Learning Engineer in the Applied AI team, you will contribute to advancing AI technology by developing and optimising new and existing AI models for specialised hardware.
Responsibilities
- Implement and train state-of-the-art machine learning models and optimise for performance, accuracy and scalability across systems comprising 1000s of accelerators
- Benchmark and profile ML models to identify performance bottlenecks
- Develop deep understanding across the software stack in order to optimise kernel implementations
- Test and evaluate new internal software releases, provide feedback to software engineering teams, make necessary code fixes, and conduct code reviews
- Design and conduct experiments on novel AI methods and evaluate results
- Collaborate with Research, Software, and Product teams
- Engage with AI community and keep in touch with the latest developments in AI
Requirements
- 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 hardware-accelerated deep learning from model training to optimisation and evaluation
- Capable of designing, executing and reporting from ML experiments
- Well-developed understanding of performance bottlenecks
- Ability to move quickly in a fast-moving field
- Enjoy cross-functional work collaborating with other teams
- Strong communicator
Nice to have
- 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, and cloud computing platforms
- Contributed to open-source projects or published research papers
Conditions
- Hybrid working model from any UK office (Bristol, London or Cambridge)
- Flexible working
- Generous leave
- Up to 5% matched pension
- Phantom equity
- Income protection and life assurance
- Electric Vehicle Scheme
- Flexible benefits including private medical insurance and dental cover