<p>We are seeking a Research Engineer to join the Wearable Agents AI team. This role will focus on creating advanced machine learning models and systems that power the Wearable AI Assistant's memory and personalization features, including multi-modal recall, personalization, recommendations, and related capabilities.</p> <p>Qualifications: Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience Proven track record of research in one or more of the following areas: deep learning, information retrieval, personalization, recommendation systems, or LLMs 5+ years experience deploying machine learning models and systems at scale in an industry setting Demonstrated up-to-date expertise in machine learning research advancements and adherence to industry best practices Advanced degree (MS or PhD) in Computer Science, Machine Learning, AI, or a related technical field, or equivalent practical experience Experience working on wearable or assistant AI technologies Expertise in multi-modal machine learning and memory systems Demonstrated software engineering capabilities with practical experience in developing and maintaining production machine learning systems Proven ability to effectively communicate complex technical concepts and collaborate productively with cross-functional teams</p> <p>Responsibilities: Research and develop state-of-the-art machine learning models and novel algorithms tailored to wearable AI assistant capabilities, with a primary focus on memory, personalization, multi-modal recall, and recommendation systems Design, prototype, and implement robust systems enabling multi-modal recall of user experiences, integrating a wide range of data sources such as text, audio, image, and sensor inputs for contextual understanding Architect and optimize machine learning pipelines for dynamic user personalization and tailored recommendations, ensuring scalable, low-latency performance for real-world applications Conduct rigorous experimentation, ablation studies, and evaluation-analyzing model performance using quantitative metrics and qualitative feedback to drive algorithmic improvements Collaborate closely with product managers, engineers, and UX designers to align technical solutions with user needs and deliver innovative, production-ready features Monitor, maintain, and iterate on deployed machine learning systems at scale, ensuring reliability, security, and continual enhancement based on user behaviors and emerging platform data Stay at the forefront of research and industry trends in deep learning, multi-modal modeling, information retrieval, recommendation, personalization, LLMs, and related disciplines, bringing new methodologies into the team's research and development pipeline Communicate findings and contribute to technical documentation, internal presentations, and external publications where appropriate</p>