2 days ago
Pune, India or Bengaluru, IndiaSenior / Staff+
Responsibilities
- Lead daily pre-merge testing, simulation-failure triage, task prioritization, team execution, and closure of critical issues.
- Analyze logs, traces, metrics, videos, and state transitions to identify root causes across software, simulation, configuration, infrastructure, analyzer, and evaluator failures.
- Use source-control history, failure patterns, signal evidence, and code-impact analysis to bisect regressions and identify culprit changelists.
- Develop Python automation for log analysis, failure classification, regression isolation, automated bisection, reporting, signal processing, visualization, debugging, and validation.
- Apply agentic AI workflows to correlate diagnostic evidence, identify patterns, summarize failures, recommend investigation paths, and improve triage efficiency.
- Coordinate with development, systems, simulation, validation, tools, and infrastructure teams; track quality metrics, communicate risks, mentor engineers, and establish consistent triage practices.
Requirements
- B.Tech. or equivalent degree in computer science, computer engineering, electrical and computer engineering, electrical and electronics engineering, automotive engineering, or a related field.
- 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.
- Expertise in bisection using Git, CI/CD pipelines, build artifacts, and change history to identify and validate culprit changelists.
- Demonstrated ability to apply agentic AI workflows.
- 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.
- Preferred experience with AI agents or LLM-based workflows for log analysis, evidence correlation, failure classification, automated bisection, or root-cause investigation.
- Preferred familiarity with GitLab CI, Jenkins, Linux, Docker, Kubernetes, cloud platforms, and large-scale simulation infrastructure.
- Working knowledge of Automotive SPICE processes and ISO 26262 functional-safety requirements in ADAS software verification and validation.
- Understanding of ADAS regulatory standards and consumer-safety assessment protocols, including Euro NCAP, UNECE DCAS, GSR, and applicable UNECE homologation regulations.
- Prior ownership of a validation, failure-triage, bisection, or pre-merge quality program with end-to-end continuous improvements.
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.