Applied Science Intern

About the Role

Join UiPath's dynamic AI team as an Applied Science Intern and contribute to developing cutting-edge AI products that empower businesses globally. UiPath, a leader in enterprise automation, champions a "robot for every person" vision, and its AI team is central to building intelligent automation solutions using state-of-the-art machine learning. In this role, you will work alongside experienced researchers and engineers, focusing on projects that advance AI in areas like Natural Language Processing, Computer Vision, and Reinforcement Learning. This internship offers an exceptional opportunity for hands-on experience in a fast-paced, collaborative environment, where your contributions will directly influence product development. You will receive mentorship from field experts, learn the entire AI product lifecycle, and help shape technologies that are transforming business operations.

Responsibilities

• Research and develop machine learning models and algorithms to address specific product challenges in areas such as NLP, Computer Vision, or Reinforcement Learning.
• Contribute to the design, implementation, and evaluation of AI prototypes and features, ensuring scalability, efficiency, and robustness.
• Analyze large datasets, identify patterns, and extract meaningful insights to inform model development and product improvements.
• Collaborate with experienced Applied Scientists and Machine Learning Engineers to translate research ideas into practical solutions.
• Participate in code reviews, technical discussions, and knowledge-sharing sessions to foster a culture of continuous learning and improvement.
• Document research findings, model specifications, and experimental results clearly and concisely.
• Stay up-to-date with the latest advancements in AI/ML research and proactively propose innovative approaches.

Requirements

• Currently enrolled in a Bachelor's, Master's, or Ph.D. program in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related quantitative field.
• Strong foundational knowledge of machine learning concepts, algorithms, and statistical methods.
• Proficiency in at least one major programming language such as Python or Java.
• Experience with relevant machine learning libraries/frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Familiarity with data manipulation and analysis tools (e.g., Pandas, NumPy).
• Basic understanding of Natural Language Processing, Computer Vision, or Reinforcement Learning concepts.
• Excellent problem-solving skills and a strong analytical mindset.
• Ability to work independently and collaboratively in a team environment.
• Good communication skills, both written and verbal.