Research Scientist Internship (Global AI Center, Ann Arbor)

About Global AI Center, Ann Arbor

The Global AI Center in Ann Arbor, inaugurated in March 2022, focuses on cutting-edge AI research with the goal of developing impactful and responsible AI, with a mission to propel technological innovations, advance scientific discovery, and benefit 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.
 

Responsibilities

• Lead, collaborate, and/or execute innovative AI research projects.
• Contribute to the development of advanced machine learning models, algorithms, architectures, and datasets that drive impactful AI solutions.
• Communicate research findings to both experts and broader audiences.
• Closely work with hosts and team members on research/engineering projects.
• Contribute to creating demos or systems demonstrating the practical applications and real-world effectiveness of AI innovations.
• Publish project outcomes as top-tier conference/journal papers or technical reports.

 

Focus Areas
Currently, we have two focus areas in research:

1. Develop AI agents capable of helping human users by solving tasks in complex environments, harnessing the latest advances in foundational models (such as large language models and large multimodal models), sequential decision-making, reinforcement learning, and multimodal AI.

2. Advance research and development of foundation models (e.g., 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.

Some relevant research topics are listed below (but not limited to):

1. Natural Language Understanding
- Large Language Models
- Compute-efficient training algorithms and architectures
- Curating and building large-scale high-quality datasets/benchmarks automatically
- Improving instruction following and text generation via advanced instruction tuning and RLHF
- Reasoning
- Code generation
- LLM Agents
- Dialogue Systems

2. Reinforcement Learning
- RL + Language
- LLM Agents
- Compositional task generalization
- AI agents/assistants for real-world applications
- Hierarchical reinforcement learning/planning/imitation learning
- Meta/multi-task/transfer reinforcement learning
- Reinforcement learning from human feedback (RLHF)
- Offline reinforcement learning

3. Multimodal learning
- Vision-language grounding
- Video understanding
- Deep generative models (images, videos, text, etc.)

 

Minimum Qualifications

• Expertise/knowledge in state-of-the-art AI research topics and methodologies.
• Motivation to lead/contribute to impactful research/engineering projects. 
• Proficiency in the use of deep learning frameworks and strong programming skills.
• Excellent communication skills, proficient at clearly presenting technical concepts and methodologies, experimental results, and key insights.

Preferred Qualifications

• Publications in major machine learning conferences.
• Extensive project portfolio.
• Experience in building new datasets, deriving insights from datasets, and demonstrating proactive data-driven problem-solving skills.
• Experience in large-scale learning, parallelism, high-performance implementations, large-scale model development, and interactive systems.
• Strong mathematical insights.
• Experience in contribution to high-impact open-source projects.
• Industry experience in applying AI research in a practical, product-oriented environment, with a focus on deliverable outcomes and real-world problem-solving. 
• Currently (or was previously) enrolled in an advanced degree program (e.g., MS or PhD) in a relevant field.

 

Successful candidates have the following traits:

• Creative Problem Solver: Exceptional skills in analyzing intricate issues and formulating innovative AI technologies and methodologies.
• Purpose-Driven Research: Strong desire to engage in research that yields practical solutions and meaningful contributions to the company and the society at large.
• Results-Oriented: Strong commitment to transforming research insights into impactful, actionable outcomes.
• Collaborative Spirit: A drive to work synergistically with a diverse group of researchers and practitioners, harmonizing individual strengths into a unified force for innovation.

Recruiting Process

• 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.