Research Engineer (CV/ML)

Position Summary

Were 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