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NetraDyne

AI-powered fleet safety dash cam analytics

Senior QA Engineer - Vehicle Networks and Telematics

Test Automation EngineeringConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listedIn-person

About the Job

Requirements

  • At least 3 years of experience in QA or validation of embedded, automotive, telematics or IoT products, including substantial hands-on CAN work and ownership of test strategy, traceability and release-readiness decisions.
  • Strong CAN 2.0A/B fundamentals, including 11-bit and 29-bit identifiers, arbitration, error handling and bus-off behavior.
  • Hands-on expertise in SAE J1939 and working knowledge of OBD-II.
  • Experience interpreting DBC files and validating raw frames, decoded signals, scaling, timing and invalid-data behavior.
  • Hands-on experience with one or more CAN analysis and simulation platforms, such as Vector CANalyzer/CANoe, Intrepid Vehicle Spy, PEAK PCAN or Kvaser, and scripting in Python, CAPL or an equivalent language.
  • Strong embedded-system debugging and root-cause analysis skills across hardware, firmware, network and backend boundaries, and ability to explain technical risks and evidence clearly to cross-functional stakeholders.

Responsibilities

  • Own validation strategy and release qualification for CAN-based vehicle features, new vehicle onboarding and customer configurations.
  • Establish requirement-to-test coverage for vehicle signals, diagnostics, power modes and end-to-end telematics workflows.
  • Investigate signal mismatches, intermittent CAN failures and vehicle-specific field issues to determine root causes.
  • Develop reusable CAN simulation, trace replay, fault-injection and automated regression capabilities.
  • Provide technical guidance through test-design reviews, automation reviews and structured defect triage.
  • Validate SAE J1939 PGN/SPN decoding for vehicle speed, odometer, engine hours, fuel, DM1/DM2 faults and other supported signals.
  • Validate OBD-II modes, PIDs, DTCs and VIN handling using SAE J1979 and ISO 15765-4.
  • Verify scaling, offsets, units, ranges, rollover, timeouts, stale data, source-address handling and not-available values.
  • Test J1939 transport protocol (BAM and RTS/CTS), request messages, address claim and bus-off recovery.
  • Correlate device-decoded values with trusted CAN tools and OEM or dealer diagnostic systems.
  • Validate wake-up on CAN traffic and ignition, sleep after bus silence, shutdown and recovery across supported configurations.
  • Measure quiescent current and verify the device causes no unwanted wake-ups, excessive bus traffic or vehicle battery drain.
  • Validate ignition (KL15) and battery (KL30) scenarios, brownouts, power interruptions and repeated ignition cycles.
  • Set up and execute bench, HIL and in-vehicle tests using CAN interfaces, programmable power supplies, ignition control and harnesses.
  • Simulate ECUs, replay recorded traces, inject protocol and signal faults, and reproduce field scenarios in the lab.
  • Validate integrations across commercial and passenger vehicles, makes, models and model years, including in fleet yards and at customer sites.
  • Validate CAN-derived data from vehicle bus to cloud for fleet features such as fuel consumption, fuel-level trends, idle fuel, refueling and suspected fuel theft.
  • Validate CAN-derived inputs supporting ELD/HOS workflows, including engine power state, vehicle motion, distance, engine hours and malfunction scenarios, against product requirements and the FMCSA ELD rule.
  • Support validation of EV signals (state of charge, charging state and energy usage) and OTA/firmware regression for vehicle-specific releases.
  • Translate product requirements, protocol specifications, DBC files and field use cases into risk-based, traceable test plans.
  • Build maintainable Python, CAPL or equivalent automation for CAN simulation, trace replay, signal verification, negative testing and regression.
  • Analyze CAN traces, device and application logs, power measurements and cloud telemetry to isolate failures across the vehicle, harness, firmware, configuration and cloud layers.
  • Create defect reports with reproducible steps, trace evidence, expected versus actual behavior and impact.
  • Work with development teams to close gaps in testability, diagnostics and observability.

Desired Qualifications

  • Understanding of CAN wake-up and sleep behavior, ignition concepts, power-state transitions and sleep-current measurement.
  • Comfort working with vehicle harnesses, multimeters, programmable power supplies and bench equipment.
  • Working knowledge of UDS (ISO 14229) and ISO-TP, including diagnostic sessions, reading and clearing DTCs, and reading data identifiers.
  • Awareness of CAN FD and experience with HIL systems or ECU/network simulation.
  • Exposure to J1708/J1587 on older heavy-duty vehicles.
  • Python experience with libraries and frameworks such as python-can and pytest, including data-driven and parameterized testing.
  • Experience with Linux-based environments, SocketCAN and can-utils (candump, cansend), shell tools, log analysis, Git and code-review workflows.
  • Experience in fuel management, ELD/HOS (FMCSA), vehicle diagnostics, EV telemetry or other CAN-based fleet features.
  • Experience using AI-assisted tools for test design, log triage or trace analysis.
  • Experience with Jira, Confluence and CI-based automated testing for traceability, defect management and qualification reporting.

About the Company

NetraDyne

AI-powered fleet safety dash cam analytics

NetraDyne provides AI-powered safety solutions for commercial fleets. Its main product is an AI-driven dash cam that analyzes driver behavior in real time, detects risky actions (like texting or drowsiness), and issues audio alerts to help drivers correct behavior. Beyond recording, it converts data into actionable insights for fleet managers, enabling remote monitoring, driver coaching through recommended videos, and rewards for safe driving. Revenue comes from selling the technology and a subscription-based software platform for proactive safety, traffic-violation detection, and performance analytics. The company differentiates itself by focusing on proactive, data-driven safety and coaching to prevent incidents, rather than merely recording events. Its goal is to reduce accidents and create safer, smarter roadways for commercial fleets.

Company size
1,001-5,000
Company stage
Series D
Total funding
$352.5M
Headquarters
San Diego, CA
Founded
2015

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