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Open since Sep 2026

Software Verification & Validation EngineerPlusAI · Santa Clara

Software Verification & Validation Engineer

Santa ClaraHybridFull-time

AI Summary

Designs safety and comfort metrics, builds simulation test suites, and automates testing frameworks for autonomous vehicle software modules including perception, prediction, planning, and control.

About this role

PlusAI is a Physical AI company pioneering AI-based virtual driver software for factory-built autonomous trucks. Headquartered in Silicon Valley with operations in the United States and Europe, Plus was named by Fast Company as one of the World’s Most Innovative Companies. Partners including TRATON GROUP’s Scania, MAN, and International brands, Hyundai Motor Company, Iveco Group, Bosch, and DSV are working with Plus to accelerate the deployment of next-generation autonomous trucks. If you’re ready to make a huge impact and drive the future of autonomy, Plus is looking for talented individuals to join its fast-growing teams.

Key Responsibilities

  • Performance Metrics Definition: Design, formalize, and implement quantitative safety and comfort metrics for AV algorithm modules (Perception, Prediction, Motion Planning, and Control). Translate high-level operational design domains (ODDs) and potential failure modes into automatable, objective criteria.

  • Simulation Test Engineering: Build, execute, and maintain complex open-loop and closed-loop simulation test suites to stress-test software releases, evaluate edge cases, and benchmark module-level and system-level performance.

  • Failure Triage & Root Cause Analysis: Analyze large-scale simulation outputs to distinguish genuine software regressions from simulation artifact anomalies. Localize root causes to specific modules and deliver actionable diagnostic reports to development teams.

  • Test Framework Automation: Write production-grade Python scripts and extend core testing frameworks to automate test execution, data ingestion, and statistical reporting.

  • CI/CD Pipeline Integration: Integrate automated regression suites into continuous integration pipelines (e.g., Jenkins) for seamless, multi-stage testing across daily builds.

  • Data-Driven Quality Standards: Establish reproducible testing practices, enforcing strict artifact hygiene, dependency management, and quantitative validation over visual inspection.

Required Skills:

  • Autonomous Vehicle Domain Knowledge: Deep understanding of AV stack architecture—including perception, prediction, motion planning, and control—along with their associated failure modes and physical constraints.

  • Simulation & Test Design: 2+ years of hands-on experience authoring scenarios and executing test cases within Software-in-the-Loop (SIL) or Hardware-in-the-Loop (HIL) simulation platforms.

  • Quantitative & Safety Metrics Analysis: Demonstrated experience defining and evaluating kinematic and spatial metrics (e.g., Time-to-Collision [TTC], headway, jerk/acceleration profiles, lateral error) using statistical analysis across large datasets.

  • Python Development: Strong Python scripting skills for data processing, test automation, and test framework creation, with an emphasis on clean code structure and problem-solving.

  • CI/CD & DevOps Tooling: Practical experience with automated build/test pipelines (e.g., Jenkins, GitHub Actions) and visualization tools (e.g., Grafana, custom dashboards) to report test metrics.

  • Education: Bachelor’s or Master’s degree in Computer Science, Robotics, Electrical/Software Engineering, Mathematics, Statistics, or a related technical discipline.

    • Preferred Skills:

  • Standardized Map & Scenario Formats: Familiarity with lane-level maps (OpenDRIVE, Lanelet2, OSM) or scenario description standards (OpenSCENARIO); hands-on experience with spatial/geometry data pipelines.

  • Computational Geometry: Foundations in applied geometry, including polyline sampling, curvature evaluation, offsets, and polygon intersection routines.

  • Industry Simulators: Experience with open-source or commercial simulator tools (e.g., CARLA).

  • Data Querying & Storage: Proficiency with SQL and relational databases for querying bulk test results.

  • Safety Regulations & Benchmarks: Exposure to real-world crash databases (e.g., NHTSA CISS/CRSS, FARS) or standard safety testing protocols (e.g., Euro NCAP, ISO 26262/ISO 21448 SOTIF).

    • Skills

    Autonomous Vehicle StackCARLACI/CDComputational GeometryData IngestionEuro NCAPFARsGitHub ActionsGrafanaHILISO 21448 SOTIFISO 26262JenkinsLanelet2NHTSA CISS/CRSSOpenDriveOpenSCENARIOPythonSILSimulation Test EngineeringSQLStatistical AnalysisTest Framework Automation

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