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.
• Support product bring-up and mass production readiness.

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.