Computer Vision Engineer

• 5+ years of hands-on experience in computer vision (or combination of computer vision and deep learning).

• Strong proficiency with Python and deep learning frameworks (e.g., PyTorch, TensorFlow).

• Demonstrated experience deploying models for edge inference (e.g., TensorRT, ONNX, quantization, pruning).

• Ability to integrate with vendor model types & toolchains (eg: RKNN)

• Strong problem-solving skills and ability to work with messy, real-world data.

• Experience with AWS cloud services (preferred).

• Knowledge of MLOps practices, including deployment, monitoring, and model lifecycle management (preferred).

• Experience with camera sensors, color spaces, and image acquisition challenges (noise, lens distortion, exposure), as well as camera calibration and geometric transformations (preferred).

• Competitive compensation with private healthcare.

• Global Impact: A chance to shape the future of unattended retail technology across the globe

• Growth Opportunities: Fast-growing team, shape our engineering culture, mentor juniors and learn new technologies.

• Collaborative Culture: Small, passionate teams, open to new ideas and tech experimentation.

• Offices in Zurich, London, and 4 other countries.

• The usual: budget for personal development, benefits for your well-being, and some decent swag

Boost inc. is on a mission to redefine unattended retail. We are seeking a Computer Vision Engineer to join our team and help build cutting-edge solutions for our latest products, focused on consumer behaviour tracking and retail product identification. You will design and implement computer vision models that recognise, classify, and track products and behaviour, leveraging the power of edge computing in addition to cloud-based tools. Your work will directly impact how customers and retailers interact with products, improving accuracy, automation, and efficiency.

• Design, train, and optimise computer vision and deep learning models for product recognition, detection, and tracking.

• Develop robust pipelines for image preprocessing, data augmentation, and annotation at scale.

• Build and deploy models for real-time inference on edge devices as well as scalable cloud services.

• Optimize models for latency, memory efficiency, and throughput in production environments.

• Collaborate closely with cross-functional teams (backend engineers, product managers, UX/UI) to integrate solutions into customer-facing applications.

• Evaluate and benchmark models against datasets that capture the complexity of real-world retail scenarios (e.g., varying lighting, occlusion, packaging changes).

• Stay current with advancements in computer vision research and explore how they can be leveraged and applied to our product identification challenges.