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Research Engineer-ADAS

Research Engineer-ADAS

ConfidentialGurgaon / Gurugram, India
6 days ago
Job description

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.
  • 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).
  • 2–3 years in CV / ML or video‑centric software roles. Hands on in modern C++ on Linux, with strong Git and CMake.
  • Solid image processing and computer‑vision 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 producer–consumer 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, clang‑tidy, sanitizers; CI / CD (GitHub / GitLab / Bitbucket).
  • Debug / Perf : gdb, perf, valgrind

    • Skills Required

    RTMP, Gstreamer, Multithreading, Cmake, Rtsp, Ffmpeg, Valgrind, Gdb, simd, Tensorflow, Git, Pytorch, Linux, Opencv, PERF

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