AI Scientist/Engineer Intern (LG AI Research Center, Ann Arbor)
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Before you apply
LocationGlobal AI Center, Ann Arbor , Ann Arbor, Michigan, United States
Typefull-time
SeniorityEntry
DepartmentAdvanced Agent Lab
Company size501–1,000 people
PostedNov 9, 2022 · 3y ago
Verified live2h ago
May no longer be actively hiring
Posted by the company on Nov 9, 202274% of this company's open roles have been listed for 90+ days
At a glanceSummarised by Seekless from the posting.
Motivation to lead/contribute to impactful research ideas
Familiarity with state-of-the-art research topics and methods
Proficiency in deep learning frameworks and strong programming skills
Publications in major machine learning conferences
Strong mathematical insights, large-scale modeling experience and a desire to make breakthroughs
Skills
deep learning frameworks
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
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Lead/contribute to impactful research projects.
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Develop new datasets, models, architectures, and algorithms in machine learning.
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Publish scientific articles.
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Demonstrate research outcomes to internal and external users.
Topics
1.
Natural Language Understanding
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Large Language Models
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Reasoning
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Dialogue Systems
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Text Generation (Conditional Generation & Factual Generation)
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Curating and building large-scale high-quality datasets/benchmarks
2.
Reinforcement Learning
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RL + Language
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Compositional task generalization
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Hierarchical reinforcement learning/planning/imitation learning
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Meta/multi-task/transfer reinforcement learning
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Offline reinforcement learning
3.
Multimodal learning
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Vision-language grounding
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Video understanding
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Deep generative models (images, videos, text, etc.)
4.
Neural combinatorial optimization
Qualifications
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Motivation to lead/contribute to impactful research ideas.
•
Familiarity with state-of-the-art research topics and methods.
•
Proficiency in deep learning frameworks and strong programming skills.
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Nice to have publications in major machine learning conferences.
•
Nice to have strong mathematical insights, large-scale modeling experience and a desire to make breakthroughs.
Recruiting Process
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Application Review → Coding Test → Technical Interview (Online)
* The process is subject to change and we will contact you separately if you are selected to move forward with the recruiting process.