Research Engineer (LG AI Research Center, Ann Arbor)

LG AI Research · Ann Arbor

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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 engineering-driven research ideas with self-motivation.
• Develop and collaborate on impactful research projects.
• Align, scale, and demonstrate our research products to internal and external users.
• Design new or improve state-of-the-art datasets, models, architectures, and algorithms in machine learning.
• Write scientific articles and contribute to our research track record.

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 programming skills and project portfolio.
• Strong proficiency in deep learning frameworks.
• Familiarity with state-of-the-art research topics and methods.
• Nice to have strong research/publication track record.
• Nice to have strong mathematical insights, large-scale modeling experience, dataset publications, and a desire to make breakthroughs.
• Nice to have experience in large-scale learning/parallelism, high-performance implementations, and user-interactive systems.
• Nice to have an advanced degree (e.g., M.S. or Ph.D.) with publications in major machine learning conferences.

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.

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