What you'll be doing:

- Analyze vehicle-level ADAS requirements and contribute to the development of test cases, test specifications, and test plans.
- Execute ADAS software validation on simulation platforms across nominal, safety, fault-injection, and performance scenarios, ensuring test results are accurately captured and reported.
- Investigate test failures by reproducing issues, collecting diagnostic evidence, and performing first-level failure classification and root-cause analysis.
- Analyze logs, signals, traces, metrics, videos, and state transitions to identify failure patterns and help determine whether issues originate from software, simulation, configuration, infrastructure, analyzers, or evaluators.
- Perform regression analysis and code bisection using Git history and signal evidence, identify potential culprit CLs, and escalate complex regressions with clear supporting evidence.
- Work with software developers, validation engineers, simulation, tools, infrastructure, and hardware teams to investigate and resolve assigned bugs and validation issues.
- Develop Python scripts to simplify repetitive tasks such as log parsing, signal analysis, failure classification, data extraction, and reporting.
- Use AI/LLM-based tools and Agentic AI workflows to assist with log analysis, evidence correlation, failure summarization, and pattern identification.
- Contribute to failure-trend analysis, quality dashboards, and periodic validation reports, highlighting recurring issues and test gaps.

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 25 years of experience in automotive software testing, validation, or a closely related engineering role.
- Practical experience with SIL, HIL, simulation,



or software-based validation and a basic understanding of automotive verification and validation processes.
- Hands-on experience testing ADAS or automated-driving features. Experience in one or more areas such as perception, localization, prediction, planning, control, or vehicle interfaces is preferred.
- Good Python programming skills, with experience writing scripts for test automation, log parsing, data analysis, signal processing, visualization, or debugging.
- Working knowledge of Git and CI/CD environments, including the ability to inspect commits, build information, test results, and change history during regression investigation.
- Strong analytical and debugging skills, with the ability to connect evidence from multiple sources and systematically narrow down the cause of a failure.
- Familiarity with AI/LLM-based developer or engineering tools, with an interest in applying AI to debugging, validation, and automation.
- Valuable written and verbal communication skills, with the ability to document technical findings clearly and communicate issues effectively with developers and validation teams.
- Experience with Jira, Jama, DOORS, Codebeamer, or other ALM/requirements-management tools.

Ways to Stand Out from the Crowd

- Experience using AI agents or LLM-based workflows for log analysis, debugging, failure classification, evidence correlation, or test automation.
- Exposure to GitLab CI, Jenkins, Linux, Docker, Kubernetes, cloud environments, or large-scale simulation infrastructure.
- Knowledge of Automotive SPICE (ASPICE) and ISO 26262 Functional Safety concepts and their relevance to automotive software validation.
- Familiarity with ADAS regulatory and consumer safety assessment frameworks, including Euro NCAP, UNECE DCAS, GSR, or UNECE homologation requirements.
- Demonstrated ability to independently investigate technical problems, learn new tools quickly, and turn repeated manual activities into automation.
- Any certification course on Machine learning / Self-driving car.

📌 ADAS Software Testing/Validation (Pune)
🏢 Avin Systems
📍 Pune