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
