Senior Software Engineer

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

We are looking for a senior engineer to design and evolve an application-level simulator for ML workloads that directly influences hardware and software architecture decisions.

This role sits at the intersection of machine learning, systems, and hardware design. You will model real-world workloads, predict system behaviour, and provide insights that guide how our platform evolves.

Success in this role requires not only strong technical depth, but also the ability to work closely with multiple teams to ensure the simulator reflects reality and drives meaningful decisions.

The Tenjin team focuses on building advanced simulation capabilities to model and predict the behaviour of large-scale ML workloads on next-generation hardware and software platforms.

We operate at the intersection of machine learning, systems, and architecture, developing tools that enable data-driven decisions long before systems are fully built or deployed. Our work helps shape both hardware and software direction by providing early insights into performance, scalability, and efficiency.

The team brings together engineers with backgrounds in simulation, ML systems, and computer architecture. We collaborate closely with ML engineers, performance teams, and silicon architects, but operate as an independent group focused on simulation as a core capability.

• →Design and evolve an ML application-level simulator that models workload execution, system behaviour, and resource usage
• →Translate real-world workloads and benchmarking data into accurate simulation models
• →Validate simulator outputs against real system measurements and continuously improve fidelity
• →Build scalable, maintainable components that support evolving hardware and software capabilities
• →Work closely with performance engineers, ML teams, and silicon architects to ensure the simulator reflects real use cases
• →Drive adoption of the simulator as a decision-making tool across the organisation

• Strong C++ development experience, especially in performance-sensitive or large-scale systems
• Strong Python skills and familiarity with ML frameworks (e.g. PyTorch) and execution graphs
• Solid understanding of computer architecture, memory hierarchy, and heterogeneous systems
• Familiarity with ML accelerator concepts (e.g. tensor cores, compute tiles, high-bandwidth memory)

• Understanding of modern ML architectures (e.g. transformers, MoE) in training and inference
• Experience with simulators or performance models (hardware or distributed systems)
• Knowledge of parallelism models and their mapping to hardware
• Experience with profiling and debugging complex, multi-layer systems