Senior Staff AI Research Engineer, Language & Personal Intelligence

Samsung Research America•Mountain View, CA

3d•Onsite

About The Position

The Language & Personal Intelligence (LPI) Lab at Samsung Research America focuses on advancing next-generation AI systems that understand, reason, remember, and adapt to users over time. Our mission is to build deeply personalized, context-aware, and agentic intelligence that operates efficiently across devices and ecosystems, combining foundational research with real-world impact at global scale. We are looking for a Senior Staff AI Research Engineer to design, prototype, and advance AI systems for language intelligence, personalization, and adaptive reasoning. This role sits at the intersection of research and engineering, contributing to both exploratory research and scalable implementations, with an emphasis on efficient and privacy-aware on-device AI. Opportunity to work on next-generation personalized and agentic AI systems with real-world impact A collaborative environment that values both research depth and practical engineering excellence

Requirements

PhD in Computer Science, Artificial Intelligence, Machine Learning, Natural Language Processing, or a related field, or equivalent industry research experience
7+ years of experience in AI research, applied research, or advanced AI engineering
Strong experience with modern ML frameworks (e.g., PyTorch, TensorFlow, JAX)
Solid understanding of language models, retrieval-based systems, or knowledge-augmented AI
Experience designing and implementing end-to-end AI systems, from modeling to evaluation
Strong problem-solving skills and ability to work effectively in research-driven engineering environments

Responsibilities

Design and implement AI models and systems for language understanding, generation, and personalization
Develop and prototype memory-augmented and retrieval-based AI systems, including Retrieval-Augmented Generation (RAG)
Contribute to agentic reasoning and planning systems, including: Reinforcement learning–based decision making
Progressive or hierarchical planning
Personalized planning and goal-driven behavior
Research and implement self-evolving and adaptive memory mechanisms that support long-term personalization
Explore continual and incremental learning approaches that enable models to adapt over time
Contribute to on-device intelligence research, including: Efficient model architectures
Memory- and computation-aware inference
Privacy-preserving and edge-friendly learning techniques
Collaborate with cross-functional research and engineering teams to transition ideas into prototypes and product-ready solutions
Contribute to internal publications, patents, and external research venues such as NeurIPS, ICML, ICLR, ACL, and EMNLP