ADAS Senior Software tester

What you'll be doing:

• Lead requirement elicitation and own the writing of test specifications and test plans for applicable vehicle-level requirements. Create the test strategy document as per the standard STLC.
• Lead the daily execution of ADAS software validation on simulation platforms, covering nominal, safety, fault-injection, and performance scenarios across a feature or subsystem.
• Own failure triage and prioritization end-to-end technical assessment, issue tracking, stakeholder communication, and driving critical and high-impact issues through to closure.
• Analyze large-scale sensor and simulation data to identify failure trends and lead root-cause analysis across logs, traces, metrics, videos, and state transitions, isolating issues across software, simulation, configuration, infrastructure, analyzers, and evaluators.
• Lead bisections using source-control history, failure patterns, signal evidence, and code-impact analysis to identify culprit CLs and resolve complex multi-change regressions.
• Provide technical analysis of top bugs to cross-functional stakeholders including project management, hardware, and software teams, and periodically publish statistical quality reports.
• Design and build Python-based tools for log analysis, failure classification, regression isolation, automated bisection, and reporting — reusable infrastructure the broader team depends on.
• Apply Agentic AI workflows to correlate diagnostic evidence, identify patterns, summarize failures, recommend investigation paths, and improve team-wide triage efficiency.
• Track quality metrics, communicate risks, mentor engineers, and establish consistent triage practices across the team.

What we need to see:

• B.Tech. or equivalent degree in Computer Science, Computer Engineering, Electronics & Communication, Electrical Engineering, Automotive Engineering, or a related field, with 5–8 years of relevant experience in automotive software testing and validation.
• Strong expertise in HIL/SIL, simulation-based testing, automotive verification and validation workflows, and system-level root-cause analysis using signals, logs, traces, metrics, and other diagnostic evidence.
• Hands-on experience validating ADAS and automated-driving functions, including L2, L2++, L3, and/or L4 systems, across domains such as perception, localization, prediction, planning, control, and vehicle interfaces.
• Advanced Python programming skills with hands-on experience developing automation for data and signal analysis, visualization, debugging, validation tooling, failure classification, and reporting.
• Proven expertise in regression analysis and automated/manual bisection using Git, CI/CD pipelines, build artifacts, source-control history, and change analysis to identify, isolate, and validate culprit change lists (CLs).
• Demonstrated ability to apply Agentic AI workflows to engineering and validation problems, including diagnostic evidence correlation, failure analysis, pattern identification, investigation support, and automation of repetitive triage activities.
• Strong communication, technical documentation, and stakeholder-management skills, with the ability to translate complex technical and process topics into clear, actionable steps and decisions.
• Hands-on experience with Application Lifecycle Management (ALM) and requirements-management tools, such as Jira, Jama, DOORS, Codebeamer, or equivalent platforms.

Ways to stand out from the crowd:

• Experience applying AI agents or LLM-based workflows to log analysis, evidence correlation, failure classification, automated bisection, or root-cause investigation.
• Familiarity with GitLab CI, Jenkins, Linux, Docker, Kubernetes, cloud platforms, and large-scale simulation infrastructure.
• Working knowledge of Automotive SPICE (ASPICE) processes and Functional Safety (ISO 26262) requirements, with their application in ADAS software verification and validation.
• Good understanding of ADAS regulatory standards and consumer safety assessment protocols, including Euro NCAP, UNECE DCAS, GSR, and applicable UNECE homologation regulations.
• Any certification course on Machine learning / Self-driving car.