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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.

Lead a multi-site team responsible for functional post-silicon validation of AI SoCs and platforms. Plan and debug bare-metal C/C++ tests, deploy validation on first silicon, build Python automation and regression frameworks, analyze failures, improve test infrastructure, collaborate across hardware, firmware, software, and systems teams, and provide technical guidance and feedback to architects.

Develop system-level diagnostics and stress-testing software for next-generation AI SoCs. Extend Arm diagnostics technologies, build validation utilities, automate testing across server and rack-scale environments, and identify hardware failures involving CPUs, memory, accelerators, PCIe, firmware, and other platform components. Collaborate with hardware, firmware, and validation teams to improve observability, fault isolation, silicon bring-up, reliability, and performance characterization across multiple platforms and silicon revisions.

Lead post-silicon validation of advanced AI chips and platforms, including bare-metal C/C++ test development, first-silicon bring-up, debugging, and Python-based automated regression frameworks. Analyze failures, improve validation infrastructure and methodologies, collaborate across engineering teams, provide architectural feedback, and mentor engineers. Support software, firmware, production test, characterization, and new product introduction activities.

Lead power, thermal, and performance validation for AI compute silicon and systems across pre- and post-silicon environments. Develop validation strategies, automation frameworks, regression infrastructure, benchmark workloads, and reporting tools. Analyze performance data, debug hardware-software interactions, validate CPU, memory, interconnect, and high-speed I/O subsystems, and collaborate with architecture, firmware, software, and systems teams to improve product efficiency, reliability, and scalability.