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Do you enjoy solving complex automotive software failures and leading high-impact validation activities in a fast-paced engineering environment? We are looking for an experienced SWQA Lead with strong automotive domain knowledge, hands-on simulation testing expertise, advanced Python skills, and a proven ability to identify regression-causing changelists(CL’s) through systematic bisection.
This role is central to Integration testing quality and simulation failure triage. You will lead daily testing activities, analyze various failures and artifacts, identify root causes, isolate culprit changelists (CLs), and coordinate resolution across software, simulation, validation, and infrastructure teams.
As the Person in Charge (PIC), you will manage priorities, assign work, remove blockers, improve triage efficiency, and ensure release-impacting issues are driven to closure. You will also apply Agentic AI workflows to accelerate failure investigation, evidence correlation, classification, and reporting.
Lead daily pre-merge testing, simulation failure triage, task prioritization, team execution, and timely closure of critical issues.
Perform root-cause and signal-level analysis using logs, traces, metrics, videos, and state transitions to isolate software, simulation, configuration, infrastructure, analyzer, or evaluator failures.
Lead the bisections using source-control history, failure patterns, signal evidence, and code-impact analysis to identify culprit CLs and complex multi-change regressions.
Develop Python-based tools and automation for log analysis, failure classification, regression isolation, automated bisection, and reporting.
Apply Agentic AI workflows to correlate diagnostic evidence, identify patterns, summarize failures, recommend investigation paths, and improve triage efficiency.
Collaborate with development, systems, simulation, validation, tools, and infrastructure teams; track quality metrics, communicate risks, mentor engineers, and establish consistent triage practices.
B.Tech. or equivalent degree in CS, CE, ECE, EEE, Automotive Engineering, or a related field, with 8–12 years of relevant automotive software testing and validation experience.
Strong expertise in HIL/SIL, simulation testing, automotive verification workflows, and root-cause analysis using signals, logs, traces, and system-level evidence.
Hands-on validation experience with ADAS and automated-driving functions, including L2, L2PP, and L3/L4 features across perception, localization, prediction, planning, control, and vehicle interfaces.
Advanced Python skills for automation, signal processing, data analysis, visualization, debugging, and validation-tool development.
Proven expertise in bisection using Git, CI/CD pipelines, build artifacts, and change history to identify and validate culprit CLs.
Demonstrated ability to apply Agentic AI workflows.
Proven ability to lead small to mid-sized teams, work with diverse stakeholders, and drive process alignment and continuous improvement.
Excellent communication, documentation, and stakeholder management skills, with the ability to break down complex process topics into actionable steps.
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.
Prior experience owning a validation, failure-triage, bisection, or pre-merge quality program and delivering continuous improvements end-to-end.
