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- Tasks: Create and own analytical models for optical processors in AI.
- Company: Flux Computing designs cutting-edge optical processors for large AI models.
- Benefits: Competitive salary, stock options, comprehensive healthcare, and 25 days PTO.
- Why this job: Join a dynamic team driving innovation in AI with impactful projects.
- Qualifications: 7+ years in performance modeling; strong C++ and Python skills required.
- Other info: Work from our Kings Cross HQ; extra £24,000/year for short commutes.
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 Modeling 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 Modeling Engineer employer: Flux Computing
Contact Detail:
Flux Computing Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Staff Performance Modeling 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 like gem5 or SystemC. Be prepared to discuss specific projects where you've successfully implemented these tools, as this will demonstrate your hands-on expertise.
✨Tip Number 3
Network with professionals in the optical computing and AI fields. Attend relevant meetups or conferences in London to connect with potential colleagues and gain insights into the industry, which can give you an edge in your application.
✨Tip Number 4
Prepare to discuss your approach to data-driven decision-making. Be ready to share examples of how you've used analytical models to influence design choices in previous roles, as this aligns closely with the responsibilities of the position.
We think you need these skills to ace Staff Performance Modeling Engineer
Some tips for your application 🫡
Understand the Role: Before applying, make sure you fully understand the responsibilities and requirements of the Staff Performance Modeling Engineer position. Familiarise yourself with the technical skills needed, such as experience in C++ and Python, and knowledge of performance modelling.
Tailor Your CV: Customise your CV to highlight relevant experience that aligns with the job description. Emphasise your background in building performance models, your coding skills, and any leadership roles you've held. Use specific examples to demonstrate your impact in previous positions.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for AI and your understanding of the company's mission. Discuss how your skills and experiences make you a perfect fit for the role, and mention any personal or open-source projects that relate to performance analysis or simulation.
Proofread and Edit: Before submitting your application, carefully proofread your documents for any spelling or grammatical errors. Ensure that your writing is clear and concise, as this reflects your attention to detail and professionalism.
How to prepare for a job interview at Flux Computing
✨Understand the Role and Responsibilities
Make sure you have a clear understanding of the Staff Performance Modeling Engineer role. Familiarise yourself with the responsibilities such as building functional simulators and performance models, as well as workload analysis. This will help you articulate how your skills align with their needs.
✨Showcase Your Technical Expertise
Prepare to discuss your experience with performance or power models, particularly in relation to CPUs, GPUs, or ASICs. Be ready to provide examples of your coding abilities in C++ and Python, and any relevant projects that demonstrate your knowledge of machine-learning workloads.
✨Demonstrate Collaboration Skills
Since the role involves partnering closely with engineering teams, be prepared to discuss past experiences where you successfully collaborated on technical projects. Highlight your ability to communicate complex ideas clearly and how you’ve contributed to a data-driven design culture.
✨Prepare for Technical Questions
Expect to face technical questions related to computer architecture fundamentals, such as memory systems and queuing theory. Brush up on these concepts and be ready to discuss how they apply to the role, as well as any relevant tools or methodologies you've used in your previous work.
