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- Tasks: Create and own analytical models for optical processors in AI.
- Company: Flux Computing designs innovative optical processors for large AI models.
- Benefits: Competitive salary, stock options, healthcare, 25 days PTO, and a £24,000 incentive for short commutes.
- Why this job: Join a dynamic team shaping the future of computing with impactful projects.
- Qualifications: 7+ years in performance modelling; strong C++ and Python skills required.
- Other info: Work from our vibrant Kings Cross office; collaboration is key!
The predicted salary is between 43200 - 72000 £ per year.
Company Overview: Flux Computing designs and manufactures optical processors to train and run inference on large AI models. Join us in London to be part of a highly motivated and skilled team that thrives on delivering impact and innovation at speed.
The Role: We’re searching for a Staff 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:
- Ownership: Define and deliver the technical vision and roadmap for your team that unlocks key strategic technical and business goals that are essential to the success of Flux.
- Collaboration: Partner closely with all engineering teams to help shape our overall system architecture and delivery while ensuring models reflect reality and reality meets performance goals.
- Champion Modelling: Educate peers on modelling methodology and champion data-driven design culture.
- 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.
- 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.
Skills & Experience:
- 7+ years building performance or power models for CPUs, GPUs, ASICs, or accelerators.
- Proven track record providing technical leadership to a team of 5~10 engineers, resulting in significant business impact.
- Strong coding ability in C++ and Python; experience with discrete-event or cycle-accurate simulators (e.g., gem5, SystemC, custom in-house).
- Strong 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.
Compensation & Benefits:
- Competitive salary and stock options in a rapidly growing AI company.
- Based in our new 5,000 sq. ft. office in the AI hub of Kings Cross, London.
- To foster collaboration in our high-growth environment, we require all employees to work from our London HQ and live within a 45-minute commute. We offer an extra £24,000/year incentive for those living within 20 minutes.
- Comprehensive healthcare insurance.
- 25 days PTO policy plus bank holidays.
- Private access to our in-house 3D printer.
If you are passionate about pushing the boundaries of what's possible in AI and thrive in a high-energy, fast-paced environment, we want to hear from you. Apply now to join Flux and be a key player in shaping the future of computing.
Staff Performance Modelling Engineer employer: Flux Computing
Contact Detail:
Flux Computing Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Staff Performance Modelling Engineer
✨Tip Number 1
Familiarise yourself with the latest trends in optical computing and AI model training. Understanding the specific technologies and methodologies used by Flux Computing will help you engage in meaningful conversations during interviews.
✨Tip Number 2
Showcase your experience with performance modelling and simulation tools. Be prepared to discuss specific projects where you've built or improved models, especially in relation to CPUs, GPUs, or ASICs, as this will demonstrate your hands-on expertise.
✨Tip Number 3
Network with current employees or industry professionals who have experience in optical processors or similar fields. This can provide you with insider knowledge about the company culture and expectations, which can be invaluable during the interview process.
✨Tip Number 4
Prepare to discuss your approach to data-driven decision-making. Be ready to explain how you've used analytical models to influence design choices in past roles, as this aligns closely with the responsibilities of the Staff Performance Modelling Engineer position.
We think you need these skills to ace Staff Performance Modelling Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in performance modelling, particularly with CPUs, GPUs, and ASICs. Emphasise your coding skills 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 passion for AI and how your background aligns with the responsibilities of the Staff Performance Modelling Engineer role. Mention specific projects or experiences that demonstrate your ability to lead technical teams and deliver impactful results.
Showcase Relevant Projects: If you have personal or open-source projects related to simulators, ML kernels, or performance analysis, be sure to include them in your application. This can set you apart and show your hands-on experience in the field.
Highlight Collaboration Skills: Since the role involves close collaboration with engineering teams, emphasise your teamwork and communication skills. Provide examples of how you've successfully worked with others to achieve common goals in previous roles.
How to prepare for a job interview at Flux Computing
✨Understand the Company and Role
Before your interview, make sure you thoroughly research Flux Computing. Understand their products, especially the optical processors, and how they relate to AI models. Familiarise yourself with the specific responsibilities of the Staff Performance Modelling Engineer role, as this will help you tailor your answers to demonstrate your fit.
✨Showcase Your Technical Expertise
Be prepared to discuss your experience with performance modelling, particularly in relation to CPUs, GPUs, and ASICs. Highlight any relevant projects you've worked on, especially those involving C++ and Python coding, as well as your familiarity with discrete-event or cycle-accurate simulators. This is your chance to impress them with your technical knowledge.
✨Demonstrate Collaboration Skills
Since the role involves close collaboration with various engineering teams, be ready to provide examples of how you've successfully worked in a team environment. Discuss how you've contributed to shaping system architecture and how you ensure that models reflect reality while meeting performance goals.
✨Prepare for Problem-Solving Questions
Expect to face questions that assess your problem-solving abilities, particularly in workload analysis and bottleneck hunting. Be ready to discuss how you would approach identifying throughput, latency, and utilisation hot-spots, and propose architectural or scheduling fixes based on your findings.
