AI Validation Engineer — ADAS/L2 Autonomy (Hybrid)

Wayve; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Hybrid working

Relocation assistance

Equity

Learning & development

Health benefits

Job summary

Wayve in London is hiring for an AI Validation role focused on ADAS and L2 autonomy. You will translate customer, OEM, and safety requirements into practical validation evidence, building test suites across simulation and real-world environments.

The role blends hands-on technical work with delivering credible release evidence. As part of a hybrid team, you will collaborate with product, safety, and autonomy engineering to ensure robust validation, scalable pipelines, and repeatable evidence for

Qualifications

• Understand automotive validation for ADAS/L2 functions and translate requirements into validation plans.
• Hands-on in Python, evaluation pipelines, test implementation, simulation and data analysis.
• Communicate findings, risks and evidence to stakeholders clearly.
• Contribute to verification strategies across simulation and physical testing environments.

Responsibilities

• Translate product, safety, and regulatory expectations into validation strategies and acceptance criteria.
• Work hands-on with Python, evaluation pipelines, test implementation, simulation, and data analysis.
• Analyse results and communicate findings, risks, and evidence to stakeholders.

Skills

Data analysis

Communication

Problem solving

Team collaboration

Tools

Python

SQL

Evaluation pipelines

Simulation

Test automation

Job description

Wayve in London is hiring for an AI Validation role focused on ADAS and L2 autonomy. You will translate customer, OEM, and safety requirements into practical validation evidence, building test suites across simulation and real-world environments.

The role blends hands-on technical work with delivering credible release evidence. As part of a hybrid team, you will collaborate with product, safety, and autonomy engineering to ensure robust validation, scalable pipelines, and repeatable evidence for