F1-derived electronics and telemetry platform for motorsport and automotive
Motion Applied (formerly McLaren Applied) builds high-performance electronics, control systems, and telemetry software for motorsport and automotive customers, drawing on 35+ years of Formula One heritage. The tech stack — C#, C++, MATLAB/Simulink, DirectX, and Azure cloud infrastructure — reflects a heavy emphasis on real-time simulation and embedded systems. Current hiring focuses on engineering and data roles at mid-to-senior levels, aligned with active projects in control-unit PCB design, real-time telemetry platforms, and the Motion Control Toolbox; pain points around data streaming optimization and aggressive delivery timelines suggest the company is scaling infrastructure to handle higher-velocity sensor data and race-critical workloads.
Motion Applied is an electronics and software company serving motorsport, automotive, health, and transport sectors. Headquartered in Woking, UK, the company operates as a privately held firm with 201–500 employees. The product portfolio spans electronic systems design, electric drive solutions, telemetry and analytics, sensor technology, simulation tools (MATLAB/Simulink), and predictive analytics. The engineering-led organization develops both consumer-facing products and embedded systems, with particular depth in high-density PCB layout, mixed-signal design, and real-time data pipelines. Customer segments range from race teams and OEMs to broader industrial and health applications seeking F1-grade performance and reliability.
C#, C++, MATLAB, Simulink, WPF, DirectX for simulation and embedded work; Azure (Functions, API Management, DevOps, cloud security services), Jenkins, and Cisco networking for infrastructure and deployment.
Active projects include the ATLAS software suite, a real-time telemetry and analytics platform, motorsport and automotive electronic control units (PCB layout), high-speed mixed-technology PCB design, and the Motion Control Toolbox.
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Motion Applied's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
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