Machine Learning Engineer focused on Computer Vision for next-generation Spectacles, developing and deploying ML models for real-world AR applications.

Working student thesis project focused on developing efficient on-device ML models for AR glasses, specifically exploring event-based sensing and processing combined with deep learning techniques to meet strict latency, energy, and bandwidth constraints on embedded hardware. The role involves designing, prototyping, and evaluating models, exploring efficiency techniques, and demonstrating proof-of-concepts on AR hardware.

Machine Learning Engineering Intern to join the Spectacles AR engineering team, focusing on scene understanding for AR experiences. The role involves prototyping, training, and evaluating ML models for computer vision and multimodal understanding, contributing to geometric scene understanding, 3D reconstruction, semantic scene understanding, visual localisation, and connecting scene understanding to language for AR interactions. The intern will partner with mentors and cross-functional teams to integrate work into production-facing systems.

Computer Architecture Intern to implement, map, and simulate neural rendering algorithms on event-based Neural Processing Units (NPUs), perform benchmarking, and identify hardware/compiler improvements for efficiency.

Software Engineer C++ focused on AI tooling for AR application development, building agentic workflows for Lens Studio, and implementing software for compute-constrained AR glasses.

Computer Vision Engineering student worker role focused on researching and developing advanced computer vision algorithms and implementing them onto Spectacles. The role involves optimizing performance, writing clean code, and potentially publishing research or contributing to patents.