At a Glance
- Tasks: Design and own analytical models for cutting-edge optical compute systems.
- Company: Join a pioneering team innovating in AI with optical processors.
- Benefits: Enjoy a collaborative environment with opportunities for rapid innovation and impactful work.
- Why this job: Be at the forefront of AI technology, solving complex challenges in a dynamic setting.
- Qualifications: 5+ years in performance modelling; proficiency in C++ and Python required.
- Other info: Ideal for those passionate about hardware, software, and machine learning.
The predicted salary is between 43200 - 72000 £ per year.
Build the Future of AI with Optical Compute
We\’re pioneering optical processors to train and run inference on large-scale AI models. Join a team of highly motivated and skilled engineers dedicated to rapid innovation and high-impact outcomes.
The Role: Senior Performance Modelling Engineer
We\’re looking for a Senior Performance Modelling Engineer to design and own the analytical and simulation models that guide the evolution of our Optical TPU (OTPU) architecture and software. You will be instrumental in building functional and high-fidelity, cycle-accurate models of our optical compute system.
This is a high-leverage role that sits at the intersection of hardware design, software tooling, and machine learning workloads—ideal for engineers who thrive on data-driven decisions, rapid iteration, and solving complex performance challenges.
Key Responsibilities
-
End-to-End Ownership: Lead and deliver critical projects that enable major technical and business milestones.
-
Cross-Functional Collaboration: Work closely with hardware, compiler, and ML framework teams to ensure performance models are both accurate and actionable.
-
Simulator Development:
-
Build and maintain a functional simulator of the OTPU pipeline and subsystems.
-
Develop architectural and cycle-accurate simulators to identify bottlenecks and optimize throughput, latency, and utilization.
-
Benchmarking & Bottleneck Analysis: Instrument LLMs, diffusion models, and graph workloads to generate detailed traces for deep performance analysis.
-
Design-Space Exploration: Run extensive parameter sweeps to explore architectural tradeoffs, and deliver clear, quantitative insights that guide our hardware, software, and optical designs.
-
Tooling & Automation: Create robust Python/C++ tools for trace parsing, statistical analysis, and visualization. Integrate models into CI pipelines for automated performance regression testing.
End-to-End Ownership: Lead and deliver critical projects that enable major technical and business milestones.
Cross-Functional Collaboration: Work closely with hardware, compiler, and ML framework teams to ensure performance models are both accurate and actionable.
Simulator Development:
-
Build and maintain a functional simulator of the OTPU pipeline and subsystems.
-
Develop architectural and cycle-accurate simulators to identify bottlenecks and optimize throughput, latency, and utilization.
Benchmarking & Bottleneck Analysis: Instrument LLMs, diffusion models, and graph workloads to generate detailed traces for deep performance analysis.
Design-Space Exploration: Run extensive parameter sweeps to explore architectural tradeoffs, and deliver clear, quantitative insights that guide our hardware, software, and optical designs.
Tooling & Automation: Create robust Python/C++ tools for trace parsing, statistical analysis, and visualization. Integrate models into CI pipelines for automated performance regression testing.
Required Skills & Experience
-
5+ years of experience building performance or power models for CPUs, GPUs, ASICs, or custom accelerators.
-
Proficiency in C++ and Python, with hands-on experience in developing discrete-event or cycle-accurate simulators (e.g., gem5, SystemC, or custom tools).
-
Strong understanding of computer architecture fundamentals: memory systems, interconnects, queuing theory, Amdahl\’s and Gustafson\’s laws.
-
Familiarity with machine learning workloads and frameworks like PyTorch, TensorFlow, or JAX.
-
Ability to interpret RTL/schematics and discuss micro-architectural trade-offs with hardware engineers.
-
Excellent data visualization and communication skills — capable of distilling millions of simulation samples into a single, decisive insight.
5+ years of experience building performance or power models for CPUs, GPUs, ASICs, or custom accelerators.
Proficiency in C++ and Python, with hands-on experience in developing discrete-event or cycle-accurate simulators (e.g., gem5, SystemC, or custom tools).
Strong understanding of computer architecture fundamentals: memory systems, interconnects, queuing theory, Amdahl\’s and Gustafson\’s laws.
Familiarity with machine learning workloads and frameworks like PyTorch, TensorFlow, or JAX.
Ability to interpret RTL/schematics and discuss micro-architectural trade-offs with hardware engineers.
Excellent data visualization and communication skills — capable of distilling millions of simulation samples into a single, decisive insight.
Preferred Qualifications
-
Advanced degree (Master\’s or PhD) in Electrical Engineering, Computer Science, Physics, Applied Math, or a related field.
-
Open-source or personal projects involving simulators, ML kernels, or performance analysis.
Advanced degree (Master\’s or PhD) in Electrical Engineering, Computer Science, Physics, Applied Math, or a related field.
Open-source or personal projects involving simulators, ML kernels, or performance analysis.
This role offers a unique opportunity to shape the direction of optical compute at a foundational level. If you\’re excited to work at the cutting edge of hardware, software, and AI, we\’d love to hear from you.
Modelling Engineer (Mid-Staff) in Oxford employer: La Fosse
Contact Detail:
La Fosse Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Modelling Engineer (Mid-Staff) in Oxford
✨Tip Number 1
Familiarise yourself with the latest advancements in optical computing and performance modelling. Being well-versed in current trends and technologies will not only help you during interviews but also demonstrate your genuine interest in the field.
✨Tip Number 2
Network with professionals in the optical computing and AI sectors. Attend relevant conferences, webinars, or meetups to connect with industry experts and gain insights that could give you an edge in your application.
✨Tip Number 3
Showcase your hands-on experience with simulators and performance models through personal projects or contributions to open-source initiatives. This practical experience can set you apart from other candidates and highlight your skills effectively.
✨Tip Number 4
Prepare for technical interviews by practising problem-solving scenarios related to performance modelling and architectural trade-offs. Being able to articulate your thought process and solutions clearly will impress interviewers and demonstrate your expertise.
We think you need these skills to ace Modelling Engineer (Mid-Staff) in Oxford
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in performance modelling, particularly with CPUs, GPUs, and custom accelerators. Emphasise your proficiency in C++ and Python, as well as any experience with discrete-event or cycle-accurate simulators.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Discuss specific projects or experiences that demonstrate your ability to lead critical projects and collaborate cross-functionally, especially in relation to optical compute and machine learning workloads.
Showcase Relevant Skills: Highlight your understanding of computer architecture fundamentals and your ability to interpret RTL/schematics. Mention any experience you have with benchmarking, bottleneck analysis, and design-space exploration, as these are key responsibilities of the role.
Prepare for Technical Questions: Be ready to discuss your technical expertise in detail during interviews. Prepare to explain your approach to building simulators, your experience with performance analysis, and how you've used data visualisation to derive insights from complex datasets.
How to prepare for a job interview at La Fosse
✨Showcase Your Technical Expertise
Be prepared to discuss your experience with performance modelling, particularly in relation to CPUs, GPUs, and custom accelerators. Highlight specific projects where you've built simulators or models, and be ready to explain the methodologies you used.
✨Demonstrate Cross-Functional Collaboration
Since the role involves working closely with hardware, compiler, and ML framework teams, share examples of how you've successfully collaborated across different disciplines. Emphasise your ability to communicate complex ideas clearly to non-technical stakeholders.
✨Prepare for Technical Questions
Expect questions on computer architecture fundamentals, such as memory systems and queuing theory. Brush up on Amdahl's and Gustafson's laws, and be ready to discuss how these concepts apply to performance modelling in optical compute systems.
✨Highlight Your Problem-Solving Skills
The role requires solving complex performance challenges, so prepare to discuss specific instances where you've identified bottlenecks and optimised performance. Use data-driven examples to illustrate your analytical approach and decision-making process.