Senior Vision Systems Engineer

Design and implement advanced computer vision and image processing pipelines optimized for real-time consumer devices.

Collaborate with ISP, sensor, and tuning teams to optimize image quality for downstream AI and UX performance.

Develop and deploy ML models for visual recognition, enhancement, tracking, or scene understanding.

Optimize ML models for edge deployment (quantization, pruning, distillation, hardware-aware tuning).

Implement performance-critical algorithms in modern C++ for embedded platforms.

Optimize for latency, power consumption, memory footprint, and thermal constraints.

Integrate inference engines (TFLite, TensorRT, ONNX Runtime, etc.) on target SoCs.

Work closely with Android/Linux platform teams to integrate camera and AI pipelines.

Define and track KPIs: FPS, power usage, memory, startup time, and accuracy.

Profile and optimize performance across CPU/GPU/NPU/DSP.

Drive debugging of complex system-level issues in production builds.

Ensure robust unit testing and contribute to automated validation pipelines.

Mentor engineers and review architecture/design proposals.

Must have

Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field.

7-10+ years of experience in computer vision/image processing.

Proven experience shipping at least one consumer product with embedded vision/AI.

Strong C++ expertise (C++14/17/20), including performance optimization.

Strong experience with OpenCV and ML frameworks (PyTorch, TensorFlow, ONNX).

Experience deploying ML models on embedded/edge devices.

Experience with model optimization (quantization, pruning).

Strong understanding of 2D/3D geometry and linear algebra.

Experience working on embedded Linux or Android systems.

Strong debugging and performance profiling skills.

Experience optimizing for power and thermal constraints.

Nice to have

Experience with mobile SoCs (Qualcomm, MediaTek, Exynos, etc.).

Experience with CUDA / OpenCL / Vulkan / OpenGL ES / SIMD.

Experience with camera calibration and ISP interaction.

Experience building for Android Camera HAL or Yocto-based systems.

Experience with AR, computational photography, or video processing.

Experience with multi-camera systems.

Exposure to production validation and manufacturing constraints.