Formula 1 engineering and manufacturing operation with advanced simulation and ERP modernization
Aston Martin F1 Team operates a specialized motorsport engineering and manufacturing facility at Silverstone with a tech stack spanning CAD (CATIA), enterprise resource planning (SAP, IFS, Epicor), and advanced analytics (Python, PyTorch, scikit-learn, JAX). Current hiring velocity is accelerating across 27 roles, with engineering representing the largest share — indicating active investment in vehicle development and wind tunnel capabilities. Active projects reveal a dual focus: technical work on power-unit installation systems and aerodynamic development, alongside operational scaling challenges in ERP optimization, FIA cost-cap compliance, and tax process automation.
Notable leadership hires: Chief Mechanic
Aston Martin F1 Team is the racing division competing in Formula 1, based at Silverstone in Northamptonshire, United Kingdom. The organization operates across engineering, manufacturing, design, and support functions, with roughly 201–500 employees. The technology footprint reflects both competitive motorsport demands (CAD, simulation tools, real-time performance monitoring) and enterprise operational complexity (multi-system ERP platforms, payroll, compliance, and data governance). Key operational pressures include FIA cost-cap regulation adherence, tight production schedules for competitive vehicles, and integration of legacy ERP systems to support future growth.
Core platforms: CATIA (CAD), SAP and IFS (ERP), Python and PyTorch (analytics/ML), Jira (development), ServiceNow (IT operations), Power BI (reporting), Primavera P6 (project scheduling), and design tools (Adobe Suite, Blender, Unreal Engine).
Primary pain points: ERP optimization and integration, FIA cost-cap regulation compliance, high-volume payroll processing, complex tax administration, and production schedule pressures. Active projects include wind tunnel development and power-unit installation system design.
Aston Martin F1 Team'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.