AI Engineer Intern

About the Role

We are seeking a highly motivated and innovative AI Engineer Intern for either the spring or summer to join our growing team in Palo Alto. This internship offers an exceptional opportunity to work on challenging Machine Learning / Gen AI problems in the legal domain.

As an intern, you will collaborate with experienced AI researchers and engineers to solve enterprise-grade AI problems across Content AI, Document AI, and the applications built on top of them, including evaluating and fine-tuning both open-source and closed-source models, improving retrieval and reasoning quality, and testing AI systems for accuracy, reliability, and performance.

This is a hands-on opportunity to contribute to real-world AI applications, tackle fundamental challenges in deep learning, and gain exposure to production-grade AI systems.

As an intern, you will work alongside experienced engineers and professionals to solve real-world problems, create impactful AI solutions, and learn from the best in the field.

Key Responsibilities

• Assist in designing and implementing AI/ML solutions to address customer-specific challenges.
• Collaborate with senior engineers to build, test, and deploy machine learning models in production environments.
• Support the development of cloud-agnostic and secure enterprise-grade software solutions.
• Participate in troubleshooting and optimizing AI/ML models, addressing issues such as model drift and performance bottlenecks.
• Conduct research and analysis to improve model efficiency and accuracy.
• Help develop and maintain documentation for AI workflows and deployment processes.
• Provide input on product development and contribute to enhancing the AI/ML product roadmap.

Qualifications

• Currently pursuing a Bachelor's or Master's in Computer Science, Machine Learning, AI, Mathematics, Statistics, or a related field.
• Experience with traditional machine learning, deep learning, LLMs, Retrieval-Augmented Generation (RAG), or other NLP techniques.
• Familiarity with MLOps practices and tools such as MLflow for monitoring and deploying machine learning solutions.
• Exposure to real-world projects involving data analysis, predictive modeling, or automation.
• Interest in the legal technology domain or experience working with enterprise-grade systems.