Motorsport design and engineering firm competing in Formula E
Lola is a 66-year-old British motorsport manufacturer and engineering consultancy now pivoting toward electrification and hydrogen research. The tech stack reveals a heavy simulation-first engineering culture: MATLAB, Simulink, dSpace, and C/C++ dominate, paired with modern web visualization tools (React, D3.js, Plotly) and CAD/design software (Figma, Adobe). Active hiring is concentrated in engineering and mid-to-senior roles, aligned with their stated focus on car development, simulator correlation, and in-house tyre modeling—suggesting sustained R&D investment despite the small headcount.
Lola designs and manufactures racing vehicles and provides motorsport engineering consultancy from Silverstone, UK. The company competes in the ABB FIA Formula E World Championship and serves professional and customer race teams globally. Core work spans vehicle simulation, performance optimization across race events, regulatory compliance, and materials research into electric and hydrogen powertrains. The 51–200 person organization operates a lean engineering model supported by finance and operations functions, with current hiring weighted toward engineering depth.
Lola's engineering stack centers on MATLAB, Simulink, and dSpace for vehicle simulation, paired with C/C++ for custom development. Design and visualization use Figma, Adobe Creative Cloud, React, and D3.js for data visualization and web interfaces.
Current focus areas include in-house tyre modeling for simulations, simulator-to-track correlation efforts, long-term vehicle development and specification, and regulatory compliance for Formula E competition.
Lola Cars'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 →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.