Top AI & Machine Learning Jobs

Develop and maintain infrastructure tools and services to support AI research. Manage CI platforms, deploy services with Kubernetes, and utilize cloud infrastructure.

Develop and optimise large-scale ML models for Graphcore hardware, scale training/inference across thousands of accelerators, benchmark and debug performance, run experiments, collaborate with Research and Software teams, and contribute code reviews and community engagement.

Develop, implement and optimise large-scale ML models for Graphcore hardware; benchmark and scale training/inference across thousands of accelerators; test internal releases, fix and review code; design and run experiments; collaborate with Research, Software and Product teams; engage with the AI community.

Develop, optimize, benchmark and scale state-of-the-art ML models for Graphcore hardware; run large-scale distributed training (64+ accelerators); test internal software releases, identify performance bottlenecks, design and execute ML experiments, collaborate with Research, Software and Product teams, and engage with the AI community.

Develop, optimize, benchmark, and scale large AI models for Graphcore hardware; run distributed training across thousands of accelerators, design experiments, collaborate with research and software teams, and contribute performance improvements and code reviews.

Work on software connecting Graphcore accelerators to ML frameworks (PyTorch, Triton, JAX, TensorFlow). Contribute features, tests, docs, code reviews and performance improvements while learning compilers, runtimes and hardware interactions.

The Staff AI Performance Engineer will optimize performance across ARM-based architectures and distributed systems, analyzing AI workloads and collaborating to enhance system efficiency.

Validate and benchmark ML models across Graphcore's software and hardware stack. Build automated benchmarking pipelines, run open-source models, create targeted tests for numerical precision, quantisation, attention, distributed execution and model subgraphs, and debug performance and correctness issues.

Design and build test infrastructure, automation and tooling to validate a complex ML software stack across codebases, CI pipelines, simulators/emulators and hardware. Improve reliability, performance and maintainability, shape test architecture, run integration and benchmarking workloads, and mentor engineers.

Validate and benchmark ML models and stacks on Graphcore hardware. Build automated benchmarking pipelines, run open-source models, create targeted tests for numerical precision, quantisation, distributed execution and subgraphs, and debug performance and correctness across frameworks and execution environments.