System Test Engineer (Automotive AI)

TL;DR

System Test Engineer (Automotive AI): Developing and scaling embedded software verification and automation for autonomous driving systems with an accent on HiL testing, system-level validation, and CI/CD integration. Focus on building open and closed-loop replay tests, conducting root cause investigations for failures, and ensuring compliance with automotive safety standards.

Location: Hybrid (London, UK)

Company

Leading developer of Embodied AI technology creating mapless and hardware-agnostic autonomous driving products for automakers.

What you will do

• Develop automation for system functionality tests across software and hardware boundaries at component, integration, and system levels.
• Create open-loop and closed-loop replay HiL tests, reliability, and fault-injection tests, and establish traceability dashboards.
• Lead root cause investigations for failures identified during test campaigns, collaborating with hardware, software, and DevOps teams.
• Optimize CI/CD-integrated test workflows to ensure rapid, reliable, and repeatable test execution.
• Integrate third-party verification tools including JAMA, MC/DC, and MISRA, managing artifacts via Artifactory and Azure.
• Define and track quality KPIs and metrics to support data-driven validation and release decisions.

Requirements

• Hands-on experience with system-level verification and validation in robotics, embedded software, or mechatronic systems.
• Proficiency in HiL testing and tooling using CAN, Ethernet, GMSL, dSPACE, Vector, or Xylon.
• Strong proficiency in Python and experience developing automation test frameworks using pytest.
• Knowledge of automotive safety and process standards including ISO 26262, ASPICE, and SOTIF.
• Experience with CI/CD principles and related tooling such as GitLab CI, Buildkite, or Bazel.
• Must be based in London to support the hybrid working policy.

Nice to have

• Experience working with autonomous vehicle systems, ADAS, or related technologies.
• Understanding of vehicle networks (CAN, LIN, Ethernet) and diagnostic protocols (UDS, OBD-II).
• Knowledge of vehicle instrumentation, data logging, and calibration tools (Vector, dSPACE).
• Experience with data management and version control systems like Git or Jenkins.

Culture & Benefits

• Hybrid working policy combining time in London offices/workshops with working from home.
• Inclusive work environment that values diversity and embraces new perspectives.
• Fast-paced engineering culture focused on solving complex, groundbreaking AI challenges.
• Collaborative atmosphere with a focus on mutual support to deliver high-impact results.