Research Scientist (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.

The ideal candidate for this role is someone who not only excels in AI research but also thrives in a team setting, eagerly engages with cross-functional groups, and seeks to contribute to a shared vision. We look for individuals whose ambitions are matched by their drive to make a lasting difference through their work. If you have a track record of moving beyond theory to drive real-world advancements in AI, we would be excited to welcome you to our team. We do not discriminate against candidates based on nationality, sex, age, religion, disability, or other legally protected statuses.
 

Responsibilities

• Lead, collaborate, and execute innovative AI research projects, identify new directions and formulate objectives in line with our mission.
• Contribute to the development of advanced machine learning models, algorithms, architectures, and datasets that drive impactful AI solutions.
• Collaborate with a diverse team of experts, fostering an environment of open communication and shared learning. Engage in interdisciplinary research efforts that aim for high-impact outcomes and advance the field of AI.
• Publish in top-tier conferences and journals and effectively communicate our AI research findings to both experts and broader audiences.
• Contribute to creating demos or systems that highlight the team's research and engineering efforts, demonstrating our AI innovations' practical applications and real-world effectiveness while fostering interdisciplinary collaboration with other teams.
• Contribute to the development and mentoring of junior team members, fostering a culture of growth, collaboration, and shared success.

 

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

• A strong track record of research in ML and AI, with a focus on producing work that has real-world applicability.
• A Ph.D. degree or equivalent research experience with a strong publication record in renowned machine learning and AI venues.
• Expertise in state-of-the-art AI research topics and methodologies, demonstrating a thorough understanding of the field's current landscape and its future potential.
• Proficient in the use of deep learning frameworks, with strong skills in applying these tools to solve complex problems.
• Excellent communication skills, proficient at clearly presenting the research vision, technical concepts and methodologies, experimental results, and key insights to a diverse audience, with the ability to tailor the message for varying purposes and levels of technical expertise.

 

Preferred Qualifications

• Experience in pioneering novel AI breakthroughs.
• Extensive industry experience that showcases the practical application of AI research in real-world settings.
• Experience in large-scale learning, parallelism, high-performance implementations, large-scale model development, and interactive systems.
• Exceptional mathematical ability.
• Experience in building new datasets, deriving insights from datasets, and demonstrating proactive data-driven problem-solving skills.

Successful candidates have the following traits:

• Creative Problem Solver: Exceptional skills in analyzing intricate issues and formulating innovative AI technologies and methodologies.
• Strategic Vision: A strategic outlook that ensures research activities are in alignment with overarching goals of the team and company, continuously pushing boundaries to address the most pressing challenges.
• 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.

Recruiting Process

• Application Review → Coding Test → Technical Interview (Online) → Culture Fit Interview

*  The process is subject to change, and we will contact you separately if you are selected to move forward with the recruiting process.