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 depthestimation networks.
- Integrate ML models (PyTorch/TensorFlow ONNX/TensorRT/NNAPI/NPU runtimes) for DMS/ADAS events: drowsiness, distraction/gaze, phoneusage, 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.
- Collaborate with data/ML teams on dataset curation, labeling specs, training/evaluation, and model handoff.
- Work with product & compliance to meet on road reliability, privacy, and regulatory expectations.
Qualifications
- B.Tech/B.E. in CS/EE/ECE (or equivalent practical experience).
- 23 years in CV/ML or videocentric software roles. Hands on in modern C++ on Linux, with strong Git and CMake.
- Solid image processing and computervision foundations (camera models, intrinsics/extrinsics, distortion, PnP, epipolar geometry).
- Practical experience integrating CV/ML models on device (OpenCV + ONNX Runtime/TensorRT/NCNN/MediaPipe/NNAPI).
- Experience building real time pipelines for live video (GStreamer/FFmpeg, RTSP/RTMP, ring buffers), optimizing for latency & memory.
- Competence in multithreading/concurrency, lock free queues, and producerconsumer designs.
- Comfort with debugging & profiling on Linux targets.
Reporting To: Technical Lead ADAS
Requisites
- Experience with driver monitoring or ADAS features; event logic and thresholding for production alerts.
- Knowledge of monocular depth estimation, stereo matching, or structure from motion for distance estimation.
- Model training exposure (PyTorch/TensorFlow): augmentation, evaluation (precision/recall, ROC/PR), quantization/pruning, conversion to ONNX/TensorRT/NCNN.
- Hardware acceleration (GPU/VPU/NPU, Arm NEON/DSP), YOLO/RT DETR/Lightweight backbones on edge.
- Cross compiling, Yocto/Buildroot, containerized toolchains; unit tests (gtest), static analysis (clang tidy, cppcheck), sanitizers.
- Basic familiarity with MQTT/IoT, message schemas, and over the air updates.
Technical Competency
- Languages: C++, Python
- CV/ML: OpenCV, ONNX Runtime/TensorRT/NCNN/MediaPipe; PyTorch/TensorFlow (for training/eval).
- Video: GStreamer/FFmpeg, V4L2, RTSP/RTMP.
- Build/DevOps: CMake, Git, gtest, clangtidy, sanitizers; CI/CD (GitHub/GitLab/Bitbucket).
- Debug/Perf: gdb, perf, valgrind
