SKILLS & EXPERIENCE

• 3+ years building performance or power models for CPUs, GPUs, ASICs, or accelerators.
• Strong coding ability in C++ and Python; experience with discrete-event or cycle-accurate simulators (e.g., gem5, SystemC, custom in-house).
• Solid grasp of computer-architecture fundamentals: memory systems, interconnects, queuing theory, Amdahl/Gustafson analysis.
• Familiarity with machine-learning workloads and common frameworks (PyTorch, TensorFlow, JAX).
• Comfort reading RTL or schematics and discussing micro-architectural trade-offs with hardware designers.
• Excellent data-visualisation and communication skills: able to turn millions of simulation samples into one decisive slide.
• Bachelor’s+ in EE, CS, Physics, Applied Maths or related; advanced degree preferred but not required.
• Personal or open-source projects in simulators, ML kernels, or performance analysis are a significant plus.

THE ROLE

We’re searching for a Performance Modelling Engineer to create and own the analytical and simulation models that steer OTPU architecture and software evolution. You will build functional simulators as well as high-fidelity, cycle-accurate models of our optical compute system. This role is critical to explore “what-if” design spaces, and deliver insights that directly influence our software, hardware, and optical roadmaps. This role sits at the crossroads of hardware architecture, software tooling and machine-learning workload analysis, perfect for an engineer who loves data-driven decision-making and fast iteration.

RESPONSIBILITIES

• Functional Simulator: Design, build, and maintain a functional simulator of the OPTU subsystem and full pipeline.
• Performance Simulator: Design and maintain architectural & cycle-accurate models of the OPTU subsystems and pipeline. Identify throughput, latency and utilisation hot-spots; propose architectural, or scheduling fixes.
• Workload Analysis & Bottleneck Hunting: Instrument benchmarks (LLMs, diffusion, graph workloads) to collect detailed traces.
• Design-Space Exploration: Run massive parameter sweeps with your functional and to understand tradeoffs and guide the software, hardware, and optical teams. Package results into clear, quantitative analyses and design recommendations.
• Tooling & Automation: Develop Python/C++ tooling for trace parsing, statistical analysis and visualisation. Integrate models into CI so that every RTL commit gets a performance smoke test.