Job Opportunities API

The Public Ledger of Openings

← Back to the ledger

Research Scientist (LG AI Research Center, Ann Arbor)

LG AI Research
CompanyLG AI Research
CategoryData & Analytics
LocationAnn Arbor
RemoteOn-site (inferred)
EmploymentNot stated
LevelNot stated
SalaryNot stated by the employer
Posted23 Aug 2022
Last verified11 Aug 2026
SourceEmployer ATS (greenhouse)
Applications are handled by the employer, not by us.Apply on the employer's site →
Description
About LG AI Research Center, Ann Arbor LG AI Research Center, Ann Arbor was established in March 2022 and tackles cutting-edge research questions to make the world a better place. Our mission is to develop impactful and responsible artificial intelligence that benefits technological innovations, scientific discovery, and all of humanity. We encourage open communication, collaboration, diverse perspectives, and a growth-mindset. We not only hire "well-established experts" in the relevant field of AI but also look for "high-potential candidates" who can ramp up quickly on topics aligned with our mission and values. We do not discriminate against our candidates on the basis of nationality, sex, age, religion, disability, or other legally protected statuses. Responsibilities Build and lead your own research agenda. Develop new datasets, models, architectures, and algorithms in machine learning. Collaborate on impactful research projects. Publish scientific articles. Demonstrate research outcomes to internal and external users. Topics Natural Language Understanding Large language models Reasoning Dialog systems Text generation (Conditional generation, Factual generation) Curating and building large-scale high-quality datasets/benchmarks Reinforcement learning RL + Language Compositional task generalization Hierarchical reinforcement learning/planning/imitation learning Meta/multi-task/transfer reinforcement learning Offline reinforcement learning Multimodal learning Vision-language grounding Video understanding Deep generative models (images, videos, text, etc.) Neural combinatorial optimization   Qualifications Strong research/publication track record. Expertise in state-of-the-art research topics and methods. Proficiency with deep learning frameworks. Nice to have Ph.D. degree with publications in major machine learning conferences. Nice to have strong mathematical insights, large-scale modeling experience, dataset publications, and a desire to make breakthroughs.   Recruiting Process Application Review → Coding Test → Technical Interview (Online) → Culture Fit Interview (Onsite) The process is subject to change and we will contact you separately if you are selected to move forward with the recruiting process.