Working Student - Machine Learning

Responsibilities

• Project background & research context:
• Turn always-on perception into something that fits within strict power budgets
• Push more intelligence closer to the sensor, reducing latency and data movement
• Co-design models and systems that are built for edge hardware, rather than shrinking down server-scale architectures
• In this project, you will explore how to combine modern deep learning with event-based and embedded processors to push the limits of what AR glasses can do on-device. You will help answer questions such as:
• How can we architect models that are both accurate and ultra-efficient for real-world AR tasks on event-driven or low-power hardware?
• What are the right trade-offs between accuracy, latency, memory, and energy for different AR scenarios?
• How do we turn promising research ideas into practical, measurable improvements on realistic platforms and workloads?
• Your work will directly inform how future AR experiences can run locally, responsively, and efficiently on next-generation devices
• As a thesis student, you will define and drive a focused research direction in efficient on-device ML for AR, with a particular emphasis on event-driven or embedded processors. Possible directions within this space include:
• Design and prototype ML models tailored to AR use cases under embedded constraints (e.g., event-based vision models, lightweight CNNs/Vision Transformers, or hybrid frame+event pipelines).
• Set up datasets and baselines relevant to AR tasks (e.g., detection, tracking, segmentation, gesture/interaction), and define evaluation metrics across accuracy, latency, memory usage, and energy.
• Implement and train models in PyTorch, including data pipelines, training loops, and evaluation scripts that are easy to extend and reproduce.
• Explore efficiency techniques such as sparsity, pruning, quantization (PTQ/QAT), or event-based representations, and study their impact on performance-efficiency trade-offs.
• Profile models under embedded-like conditions using simulators, profiling tools, or edge accelerators to understand system-level behavior (e.g., FLOPs, latency, memory footprint, bandwidth).
• Communicate your findings through ablation studies, a clear thesis report (and optionally a paper-style write-up), and a reproducible codebase with pre-trained checkpoints.
• Expected Outcomes
• By the end of the project, you are expected to:
• Demonstrate proof-of-concepts on AR hardware (e.g., Spectacles) showcasing real-world impact
• Deliver measurable improvements in runtime performance, efficiency, and adaptability for representative AR tasks
• Provide insights into model-system co-design for low-power, on-device ML
• Contribute to ML frameworks, tooling, or deployment strategies for embedded AR systems
• Produce a high-quality thesis report (and optionally a paper-style write-up) with reproducible code and results

Requirements

• Currently enrolled in a Master's program (e.g., Computer Science, Electrical/Computer Engineering, Artificial Intelligence, Robotics, or a related field).
• Degree program allows a Master's thesis / graduation project in collaboration with an external organization.
• Strong background in:
• Linear algebra, probability, and optimization
• Deep learning fundamentals, including backpropa

Benefits

Additional Information

Snap Inc is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together. The Company's three core products are Snapchat , a visual messaging app that enhances your relationships with friends, family, and the world; Lens Studio , an augmented reality platform that powers AR across Snapchat and other services; and its AR glasses, Spectacles . The Spectacles team is pushing the boundaries of technology to bring people closer together in the real world. Our fifth-generation Spectacles, powered by Snap OS, showcase how standalone, see-through AR glasses make playing, learning, and working better together. Snap's camera supports real friendships through visual communication, self expression and storytelling. Moving forward, our camera will play a transformative role in how people experience the world around them, combining what they see in the real world, with all that's available to them in the digital world. We are looking for a Machine Learning/ Software Engineering thesis student to join our team at Snap Inc!

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