Top AI & Machine Learning Jobs

Develops software tests for machine learning workloads on Graphcore accelerator hardware. Responsibilities include creating representative workloads, analyzing numerical accuracy and performance, designing tests, improving test infrastructure, building CI/CD pipelines, debugging issues, and collaborating with development teams to improve software quality. The two-year graduate program provides practical experience across machine learning, automation, performance analysis, and simulated and physical hardware platforms.

Graduate Machine Learning Engineers will implement, train, optimize, and benchmark AI models on Graphcore accelerator hardware. Responsibilities include profiling performance, designing and explaining experiments, identifying bottlenecks, contributing fixes across models and software, and collaborating with research, software, and product teams. The two-year programme provides hands-on experience spanning low-level kernel optimization, research algorithms, accelerator systems, and large-scale AI applications.

Conduct research on AI compute, machine learning algorithms, large language models, efficient inference, numerics, world models, molecule generation, or model safety. Implement models and algorithms in Python or C++, design and analyze experiments using frameworks such as PyTorch, and communicate findings through technical presentations or research papers under experienced mentorship.

Develop and optimise state-of-the-art machine learning models for specialised hardware and systems containing thousands of accelerators. Responsibilities include model training, benchmarking, profiling, kernel optimisation, experiment design, software testing, code reviews, and collaboration with research, software, and product teams to advance AI hardware and software. The role also involves distributed training and inference, performance analysis, and engagement with the broader AI community.

Develop, train, benchmark, and optimize state-of-the-art machine learning models across systems with thousands of accelerators. Identify performance bottlenecks, optimize kernels and software, conduct AI experiments, evaluate internal releases, and collaborate with research, software, product, and hardware teams. The role requires expertise in deep learning, hardware acceleration, performance optimization, and Python or C++ development, with desirable experience in distributed training, MLOps, LLMs, and HPC technologies.

Develop and optimize state-of-the-art machine learning models for specialized AI hardware and systems containing thousands of accelerators. Responsibilities include model training, benchmarking, profiling, kernel optimization, experimentation, software testing, code reviews, and collaboration with research, software, and product teams. The role focuses on performance, scalability, distributed training and inference, and advancing next-generation AI hardware and software.

Analyze and optimize AI training and inference workloads across distributed systems. Design benchmarks, profile performance, identify bottlenecks, and optimize communication software using MPI, NCCL, UCX, libfabric, and RDMA. Translate modeling and benchmarking results into validated system improvements while coordinating technical work across hardware, software, networking, and architecture teams.

Leads performance optimization for large-scale AI training and inference workloads across distributed systems. Builds benchmarks, profiles workloads, identifies compute, memory, networking, and software bottlenecks, and develops C++ and Python tools. Coordinates complex performance improvements across hardware and software teams, validates results with reliable data, and guides engineering decisions that improve efficiency, scalability, and reliability at data center scale.

Lead performance strategy for large-scale AI training and inference systems. Investigate workload behavior, profiling, benchmarking, modeling, distributed communication, and multi-node scaling across hardware and software. Define measurement standards, guide optimization roadmaps, review C++ and Python tools, and influence system architecture and engineering priorities across teams.

Develop, operate, and maintain Kubernetes-based infrastructure and tooling to support build, test, deployment, and scaling of Graphcore's ML software stack. Manage cloud infrastructure (infrastructure-as-code), CI/CD practices, and platform services used by software teams, focusing on reliability, automation, and reducing operational toil.