Description
Job Summary
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 Team
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.
Responsibilities and Duties
- 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
Candidate Profile
Essential:
- 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)
Desirable
- 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
Benefits
In addition to a competitive salary, Graphcore offers annual leave policy, medical and dental health plans, a gym card, and employee pension (matched up to 4%). We review our benefits on a yearly basis to ensure we offer a valuable and rewarding benefits programme to our employees.