Research Scientist

Tech Lead (Agentic Search, Staff / Principal Level) - Superintelligence Lab About LG AI Research Center, Ann Arbor The LG AI Research Center in Ann Arbor, inaugurated in March 2022, focuses on cutting‑edge AI research with the goal of developing impactful and responsible AI, propelling technological innovations, advancing scientific discovery and benefiting all of humanity at large. We foster an environment that encourages open communication, collaboration, diverse perspectives and a growth mindset. Whether you are a well‑established expert or a high‑potential candidate eager to dive into topics that resonate with our mission, we welcome your contribution to our team. We seek individuals who are excellent not just in technical ability, but in their alignment with our values and the meaningful impact of their work. If you are driven by making a difference and desire your work to resonate beyond academic publications, join us in shaping the future of AI. We do not discriminate against candidates based on nationality, sex, age, religion, disability or other legally protected statuses. Responsibilities Lead, collaborate and execute innovative AI research projects, identify new directions and formulate objectives in line with the mission. Contribute to development of advanced machine learning models, algorithms, architectures and datasets that drive impactful AI solutions. Collaborate with a diverse team of experts, fostering an environment of open communication and shared learning; engage in interdisciplinary research efforts aimed at high‑impact outcomes. Publish in top‑tier conferences and journals and effectively communicate research findings to both experts and broader audiences. Create demos or systems that highlight research and engineering efforts, demonstrating practical applications and real‑world effectiveness, while fostering interdisciplinary collaboration. Mentor junior team members, fostering a culture of growth, collaboration and shared success. Focus Areas Develop AI agents capable of helping human users by solving tasks in complex environments, leveraging foundational models (large language models and multimodal models), sequential decision‑making, reinforcement learning and multimodal AI. Advance research and development of foundation models (large language models, multimodal foundation models, etc.) by developing compute‑efficient training algorithms and architectures, constructing large‑scale, high‑quality datasets automatically, and improving generalization and adaptability with transfer learning and few‑shot/zero‑shot learning. Research Topics Natural Language Understanding: large language models, compute‑efficient training algorithms, large‑scale dataset creation, instruction tuning, RLHF, reasoning, code generation, LLM agents, dialogue systems. Reinforcement Learning: RL + language, LLM agents, compositional task generalization, real‑world applications, hierarchical RL/planning/imitation learning, multi‑task/transfer RL, RLHF, offline RL. Multimodal Learning: vision‑language grounding, video understanding, deep generative models. Minimum Qualifications Strong track record of research in ML and AI with real‑world applicability. Ph.D. degree or equivalent research experience and a strong publication record in renowned machine learning and AI venues. Expertise in state‑of‑the‑art AI research topics and methodologies, demonstrating thorough understanding of the field’s landscape and future potential. Proficient in deep learning frameworks, applying these tools to solve complex problems. Excellent communication skills, able to present research vision and technical concepts clearly to diverse audiences. Preferred Qualifications Experience pioneering novel AI breakthroughs. Extensive industry experience demonstrating practical application of AI research. Experience in large‑scale learning, parallelism, high‑performance implementations, large‑scale model development, and interactive systems. Exceptional mathematical ability. Experience building new datasets, deriving insights, and proactive data‑driven problem solving. Successful Candidate Traits Creative problem solver with innovative AI methodologies. Strategic vision aligning research with overarching goals, pushing boundaries on pressing challenges. Purpose‑driven researcher with strong desire for practical solutions and societal impact. Results‑oriented, committed to transforming research insights into impactful outcomes. #J-18808-Ljbffr