AI Scientist/Engineer Intern (LG AI Research Center, Ann Arbor)

<p><strong>About LG AI Research Center, Ann Arbor</strong></p>
<p class="txt-2 color-gray"><span style="font-weight: 400;">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.</span><br> </p>
<p><strong><br>Responsibilities</strong></p>
<ul class="list txt-2 color-gray">
<li>Lead/contribute to impactful research projects. </li>
<li>Develop new datasets, models, architectures, and algorithms in machine learning.</li>
<li>Publish scientific articles.</li>
<li>Demonstrate research outcomes to internal and external users.</li>
</ul>
<p><strong><br>Topics</strong></p>
<p class="txt-2 color-gray">1. Natural Language Understanding </p>
<ul class="list txt-2 color-gray">
<li><span style="font-size: 12pt;">Large Language Models </span></li>
<li><span style="font-size: 12pt;">Reasoning</span></li>
<li><span style="font-size: 12pt;">Dialogue Systems</span></li>
<li><span style="font-size: 12pt;">Text Generation (Conditional Generation & Factual Generation) </span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Curating and building large-scale high-quality datasets/benchmarks</span></li>
</ul>
<p class="txt-2 color-gray">2. Reinforcement Learning</p>
<ul class="list txt-2 color-gray">
<li style="font-weight: 400;"><span style="font-weight: 400;">RL + Language</span></li>
<li><span style="font-weight: 400;">Compositional task generalization</span></li>
<li><span style="font-weight: 400;">Hierarchical reinforcement learning/planning/imitation learning</span></li>
<li><span style="font-weight: 400;">Meta/multi-task/transfer reinforcement learning</span></li>
<li><span style="font-weight: 400;">Offline reinforcement learning</span></li>
</ul>
<p class="txt-2 color-gray">3. <span style="font-weight: 400;">Multimodal learning</span></p>
<ul class="list txt-2 color-gray">
<li><span style="font-weight: 400;">Vision-language grounding</span></li>
<li><span style="font-weight: 400;">Video understanding</span></li>
<li><span style="font-weight: 400;">Deep generative models (images, videos, text, etc.)</span></li>
</ul>
<p class="txt-2 color-gray">4. <span style="font-weight: 400;">Neural combinatorial optimization</span></p>
<p><strong><br>Qualifications</strong></p>
<ul class="list txt-2 color-gray">
<li>Motivation to lead/contribute to impactful research ideas. </li>
<li>Familiarity with state-of-the-art research topics and methods.</li>
<li>Proficiency in deep learning frameworks and strong programming skills.</li>
<li><u>Nice to have</u> publications in major machine learning conferences.</li>
<li><u>Nice to have</u> strong mathematical insights, large-scale modeling experience and a desire to make breakthroughs.</li>
</ul>
<p> </p>
<p class="txt-2 color-point t-medium"><strong>Recruiting Process</strong></p>
<ul class="list txt-2 color-gray">
<li>Application Review → Coding Test → Technical Interview (Online) </li>
</ul>
<p class="txt-2 color-black"><span class="color-gray">*  <span style="font-weight: 400;">The process is subject to change and we will contact you separately if you are selected to move forward with the recruiting process.</span></span></p>