Interface Research Engineer

📋 Description

• • At Adaption Inc., we are pioneering the future of artificial intelligence, moving beyond static models to create systems that evolve in real-time. Our core mission is to build efficient, adaptive intelligence that is flexible, personalized, and accessible to everyone. We believe that efficiency is the key to unlocking widespread access and ensuring that AI innovation benefits the many, not just the few. This philosophy drives our commitment to talent density, bringing together the brightest and most driven individuals to push the boundaries of continual adaptation and shape the next era of intelligent systems.
• • Our research is guided by a set of core principles: **Sweat the details**, recognizing that technical excellence demands an obsession with every aspect of the system. We co-design serving, algorithms, and interfaces as an integrated whole to maximize efficiency and enable real-time adaptation. **Move with conviction**, understanding that extraordinary results require extraordinary effort. We operate with urgency as a unified team, focusing our resources on high-impact opportunities where research and practical application intersect. **Metrics that matter**, believing that the most intelligent systems are defined by their ability to interact with and learn from the world. Research ideas are rigorously tested through working products, and we prioritize real-world impact over mere publication counts. We share advancements that move the field forward, rather than optimizing solely for paper metrics.
• • The Interface Research Engineer role is central to our vision of adaptive intelligence, focusing on reimagining feedback mechanisms and user interaction. A long-standing goal in computing is to create systems that not only respond to users but also dynamically adapt their interfaces to better facilitate interaction and help users achieve their objectives. This role will be at the forefront of shifting from static outputs to dynamic, model-driven, interactive systems. You will be instrumental in driving research innovation in adaptable interfaces and making this vision a reality.
• • This is a research engineering position requiring a strong foundation in computer science. You will be tasked with fundamentally rethinking the user interface, experimenting with novel methods for collecting feedback, and designing new interaction paradigms for AI models. Your work will directly influence how users engage with and benefit from adaptive AI.
• • **Key Responsibilities include:**
• • **Drive Innovation in Adaptive Interfaces:** Conceptualize, design, and implement novel algorithms that dynamically adapt interfaces based on real-time product signals and user feedback. Develop innovative methods for users to provide feedback, thereby enhancing the performance and adaptability of our AI models.
• • **Co-design Algorithms and Interfaces:** Collaborate closely with product teams to ensure that algorithmic advancements are seamlessly integrated with user interface design, creating a cohesive and highly efficient user experience.
• • **System-Wide Optimization:** Engage in cross-functional collaboration with software, hardware, and algorithmic engineering teams to identify and implement system-wide efficiency gains. This involves understanding the entire ML stack and optimizing performance at every level.
• • **Measure Real-World Impact:** Champion a data-driven approach to research, focusing on metrics that demonstrate tangible real-world impact. Your work will be evaluated based on its contribution to user success and the practical utility of the adaptive AI systems you help build.
• • **Research and Experimentation:** Conduct cutting-edge research into new interaction models, feedback mechanisms, and adaptive interface designs. Experiment with different approaches to user interaction and data collection to uncover new avenues for AI adaptation.
• • **Algorithm Development:** Contribute to the development and refinement of algorithms that enable real-time adaptation and personalization, ensuring our AI systems remain relevant and effective in a constantly changing world.
• • **Cross-Disciplinary Collaboration:** Work effectively within a highly collaborative, talent-dense environment, sharing knowledge and insights across different engineering and research disciplines.
• • **Technical Documentation and Communication:** Clearly document research findings, system designs, and experimental results, and effectively communicate complex technical concepts to both technical and non-technical stakeholders.