Principal Applied Research Engineer/Scientist

We are seeking a Principal Applied Research Engineer to drive breakthrough innovation across self-improving systems, agentic memory, and deeply personalized autonomous multi-agent systems. You will shape next-generation intelligent architectures that learn continuously, retain and reason over persistent memory, coordinate across agents, and translate frontier research into scalable product experiences. This unique opportunity places you at the forefront of innovation, working on projects that redefine user experiences through advanced machine learning. Advance self-improving systems that learn from deployment signals, adapt over time, and optimize performance autonomously in real-world environments. Design and operationalize agentic memory frameworks that enable long-horizon reasoning, contextual continuity, personalization, and durable knowledge across interactions. Architect deeply personalized autonomous multi-agent systems capable of coordination, task decomposition, tool use, and dynamic collaboration to solve complex user problems. Lead product-oriented research and rapid prototyping, translating foundational advances into validated user experiences and production-ready architectures. Partner cross-functionally to influence platform strategy, define technical direction, and deliver scalable ML systems that integrate tightly across hardware, software, and services. MSc or PhD in Computer Science, Machine Learning, or related field; or equivalent practical experience delivering zero-to-one LLM productization. Deep expertise in transformer-based LLMs and multi-modal foundation models, with a focus on integrating text, vision, and audio with cross-modal attention. Hands-on mastery across the model lifecycle, including pre-training, fine-tuning, performance optimization, and safety alignment. Expertise in designing AI agents for complex reasoning while navigating the unique challenges of on-device compute constraints. Demonstrated ability to influence high-level architecture decisions and effectively advocate for novel research directions to product leadership.

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