Senior Vision Systems Engineer

Luxoft; Malaysia; On-site; MYR 180,000 - 260,000; Full time

Job summary

Luxoft Germany seeks an experienced Computer Vision Engineer to design and optimize real-time CV pipelines for consumer devices. You will work with ISP, sensors, and tuning teams to enhance image quality for downstream AI and UX, and deploy ML models on edge hardware.

You will optimize models for power, latency, and memory, implement algorithms in C++, and collaborate across Android/Linux platform teams to ensure robust production readiness.

Qualifications

• Bachelor's or Master's in CS, EE or related field.
• 7-10+ years of experience in computer vision/image processing.
• Proven experience shipping consumer products with embedded vision/AI.
• Strong C++ expertise (C++14/17/20) and performance optimization.
• Experience with OpenCV and ML frameworks (PyTorch/TF/ONNX).
• Experience deploying ML models on embedded/edge devices.

Responsibilities

• Design and implement advanced CV and image pipelines for real-time devices.
• Collaborate with ISP, sensor, and tuning teams to optimize image quality.
• Develop and deploy ML models for visual recognition and tracking.
• Optimize ML models for edge deployment (quantization, pruning).
• Implement performance-critical algorithms in modern C++ for embedded platforms.
• Profile and optimize performance across CPU/GPU/NPU/DSP and power/thermal usage.
• Integrate inference engines on target SoCs and work with Android/Linux teams.
• Define KPIs like FPS, power usage, memory, startup time, accuracy.

Skills

10+ years CV/vision

C++ performance

OpenCV expertise

ML frameworks experience

Edge device deployment

Profiling & debugging

Education

Bachelor's or Master's in CS/EE

Tools

OpenCV

CUDA/OpenCL

Vulkan/OpenGL ES

Job description

Project description

Designing, optimizing, and productizing high-performance computer vision and AI pipelines for consumer device - with focus on real-time performance, power efficiency, and production robustness across embedded platforms.

Responsibilities

• 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.

SKILLS

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.