Staff Performance Modelling Engineer at Flux

San Francisco, California, United States

Apply Now

£167,000 – £204,000Compensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Optical Computing, Hardware, Machine LearningIndustries

Requirements

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

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

Key technologies and capabilities for this role

Performance ModellingFunctional SimulatorsCycle-Accurate ModelsPythonC++Workload AnalysisLLMsDiffusion ModelsGraph WorkloadsTrace ParsingStatistical AnalysisVisualizationCI IntegrationRTLHardware Architecture

Questions & Answers

Common questions about this position

What is the salary range for the Staff Performance Modelling Engineer position?

The salary range is £167K - £204K.

Is this role remote or onsite?

The role is onsite.

What skills and experience are required for this position?

Candidates need 7+ years building performance or power models for CPUs, GPUs, ASICs, or accelerators, strong coding in C++ and Python with simulator experience, strong computer-architecture fundamentals, and familiarity with machine-learning workloads.

What kind of leadership is expected in this role?

The role requires proven technical leadership to a team of 5~10 engineers, resulting in significant business impact, along with defining and delivering the technical vision and roadmap.

What makes a strong candidate for this Staff Performance Modelling Engineer role?

Strong candidates have 7+ years in performance modeling, technical leadership experience, expertise in C++/Python and simulators, computer architecture knowledge, ML workload familiarity, and personal projects in simulators or performance analysis.

Flux

AI-powered platform for PCB design

About Flux

Flux.ai provides a platform for designing and building printed circuit boards (PCBs) within the electronic design automation (EDA) market. The platform features an AI-powered assistant named Copilot, which helps users streamline the PCB design process, making it more efficient. Users can sign up for free and access basic features, with the option to upgrade to premium subscription plans for additional services. This freemium model allows engineers, designers, and electronics enthusiasts to engage with the platform at their own pace. Flux.ai differentiates itself from competitors by integrating AI assistance directly into the design workflow, enhancing user experience and accessibility in PCB design.

San Francisco, CaliforniaHeadquarters

2019Year Founded

$11.7MTotal Funding

EARLY_VCCompany Stage

Hardware, Consumer Software, AI & Machine LearningIndustries

51-200Employees

Benefits

Remote Work Options

Risks

Over-reliance on AI designs may lead to quality control issues and vulnerabilities.

New features like Smart Vias may increase complexity, potentially confusing users.

Intense competition from GenAI tools like SnapMagic could divert users from Flux.

Differentiation

Flux offers the first AI-powered hardware design assistant integrated into a PCB tool.

Flux's Copilot provides personalized design recommendations, enhancing user experience and efficiency.

Smart Vias technology simplifies high-density PCB designs, setting Flux apart from competitors.

Upsides

Increased adoption of AI tools boosts demand for Flux's innovative design platform.

Remote work trends align with Flux's cloud-based SaaS model, enhancing collaboration.

Generative AI advancements create opportunities for Flux to lead in hardware design.

Land your dream remote job 3x faster with AI

Try Jobo Free