Edge AI Engineer

Position Summary

We’re building safety first video telematics products (ADAS / DMS / driver behavior analytics) that run efficiently on edge devices inside commercial vehicles. You will write modern C++ software, integrate and optimize CV / ML pipelines, and ship reliable, low latency perception features such as driver monitoring and distance estimation from camera feeds.

Key Responsibilities

• Own C++ software modules for on device video capture, preprocessing, inference, and post processing on Linux.
• Implement classical image processing pipelines (denoise, resize, color space, undistortion) and CV algorithms (keypoints, homography, optical flow, tracking).
• Build and optimize distance / spacing estimation from monocular / stereo camera(s) using calibration, geometry, and / or depth‑estimation networks.
• Integrate ML models (PyTorch / TensorFlow → ONNX / TensorRT / NNAPI / NPU runtimes) for DMS / ADAS events : drowsiness, distraction / gaze, phone‑usage, smoking, seat belt, etc.
• Hit real time targets (FPS / latency / memory) on CPU / GPU / NPU using SIMD / NEON, multithreading, zero copy buffers.
• Write clean, testable C++, CMake builds, and Git based workflows (branching, PRs, code reviews, CI).
• Instrument logging / telemetry;

debug with gdb / addr2line, sanitize and profile with perf / valgrind.

Qualifications

Reporting To : Technical Lead ADAS

Requisites :

event logic and thresholding for production alerts.

unit tests (gtest), static analysis (clang tidy, cppcheck), sanitizers.

Technical Competency :

PyTorch / TensorFlow (fortraining / eval).

CI / CD (GitHub / GitLab / Bitbucket).