System Modeling & Integration Engineer

About the role

This role focuses on hands-on integration, debugging, optimization, and validation of ADAS algorithms on real embedded hardware with minimal dependency on algorithm developers. We are seeking a senior ADAS Algorithm Integration Engineer (Individual Contributor) with strong understanding of ADAS perception and planning pipelines and deep hands-on experience integrating ADAS algorithms into production automotive ECUs.

Responsibilities

• Integrate perception, fusion, localization, and planning algorithms into automotive ADAS platforms.
• Independently analyze algorithm data flows, state machines, and outputs to debug functional and performance issues.
• Redesign memory layouts, buffers, stack/heap usage, and partitioning to fit PoC algorithms into production ECUs.
• Analyze and optimize RAM/ROM usage, cache behavior, memory bandwidth, and DMA strategies.
• Investigate timing overruns, jitter, latency, and execution bottlenecks.
• Optimize scheduling, task priorities, and IPC using AUTOSAR OS or QNX.
• Define embedded constraints for algorithms including CPU budgets, memory limits, execution deadlines, and interface contracts.
• Work closely with base software teams on AUTOSAR Classic/Adaptive, QNX, BSPs, and middleware.
• Perform deep debugging using Trace32, Vector CANoe/CANalyzer, GDB, and profiling tools.

Requirements

• Familiarity with QNX Momentics is a plus, but not a required skill.

Qualifications

• Bachelor’s or Master’s degree in EE, CE, CS, Robotics, or related field.
• 5+ years of experience in ADAS or embedded automotive software.
• Strong understanding of ADAS domains including perception, fusion, localization, and planning.
• Hands-on experience with C/C++ and Python.
• Experience with AUTOSAR (Classic or Adaptive) and/or QNX.
• Strong background in embedded memory and timing optimization.
• Experience debugging complex automotive ECUs.
• Exposure to SIL/HIL and vehicle testing environments.

Preferred Skills

• Experience with camera, radar, and lidar sensor integration.
• Knowledge of middleware such as DDS, SOME-IP, or RTPS.
• Familiarity with ISO 26262, ASPICE, and automotive cybersecurity.
• Experience with Git, Jenkins, and CMake/Bazel.