Computer Vision Engineer

Computer Vision Systems Developer

Type: [Full-Time]

Your Mission

As a Computer Vision Systems Developer, you will be the critical link between AI research and real-world hardware. You won't just train models; you will make them run blazingly fast in production. Your primary focus will be optimizing, deploying, and maintaining robust vision pipelines on [edge devices / cloud infrastructure / robotic platforms] to ensure our systems operate flawlessly in real-time.

The Tech Stack

• Languages: Python, modern C++ (C++14+)
• Frameworks: PyTorch or TensorFlow, OpenCV
• Deployment & Optimization: TensorRT, ONNX, OpenVINO, CUDA
• Infrastructure: Linux, Docker, [GStreamer / V4L2], [AWS/GCP]
• Hardware: NVIDIA Jetson, Edge TPUs, RGB/Depth Cameras, LiDAR

The Day-to-Day

• Build the Pipeline: Architect and maintain end-to-end computer vision workflows, from camera sensor acquisition to model inference and post-processing.
• Squeeze Out Performance: Profile code and optimize deep learning models (quantization, pruning) to hit strict real-time latency and FPS targets on constrained hardware.
• Integrate Software & Hardware: Seamlessly connect vision algorithms with external sensors, edge compute boards, and broader system architecture.
• Write Production Code: Deliver clean, thread-safe, and highly efficient C++ and Python code.
• Monitor & Iterate: Build automated CI/CD pipelines to test vision models, track data drift in the wild, and push over-the-air (OTA) updates.
• Collaborate: Work hand-in-hand with Data Scientists to inform model architecture decisions based on actual hardware limits.

What You Bring

• 3+ years of professional software engineering experience with a heavy focus on computer vision and ML deployment.
• Deep expertise in writing production-ready C++ and Python.
• Proven hands-on experience optimizing models using tools like TensorRT, ONNX, or CUDA.
• A strong grasp of multi-threading, memory management, and concurrent programming in Linux environments.
• A degree (BS, MS, or Ph.D.) in Computer Science, Robotics, Electrical Engineering, or a related field.

Bonus Points

• You have successfully deployed ML models onto edge devices (NVIDIA Jetson, Coral, Raspberry Pi).
• You have experience with 3D computer vision, point clouds, or SLAM.
• You know your way around camera hardware, ISP tuning, and video streaming protocols (GStreamer).
• You have background experience in [insert your specific industry: e.g., autonomous vehicles, ag-tech, medical imaging].