Datacor - Data Science Summer 2027 Internship Program

Remote - USA4w ago

Remote Intern NA Cloud Computing Artificial Intelligence Software Engineering Intern AI Engineer Data Science Intern C++Java Python Learning & Development AWS

Requirements

• Currently enrolled in a Master’s of PhD program in Computational Data Science, Computer Science, Electrical/Computer Engineering, or related fields. • A self-starter with the ability to work independently and within a team. • Experience in a professional environment, including prior internship experience is preferred. • Expertise in the latest Large Language Models (LLMs) and AI advancements. • Proactive in staying updated with evolving AI trends and new LLM releases. • Skilled at diagnosing and solving complex, ambiguous problems with curiosity and a product-focused mindset. • Strong communication and presentation skills, able to convey complex analysis clearly and actionably. • Results-oriented, creative thinker who takes initiative and delivers practical solutions. • Committed to inclusivity, respecting diverse perspectives to foster innovation and build great products. • Proven expertise in implementing and fine-tuning generative AI LLMs, with strong NLP knowledge including embeddings, GPT, and Transformer models. • Familiarity with cloud computing and AI deployment on AWS or Google Cloud. • Solid foundation in deep learning, machine learning, statistics, and optimization from coursework or practical experience. • Proficient in Python & C++/Java programming through academic or work experience. • This is a remote internship with potential travel, depending on location, schedule, and business needs. • EOE Statement:

Responsibilities

• Collaborate with stakeholders to identify new AI opportunities and create intelligent AI applications. • Lead MVP development using a test-and-learn approach with cross-functional teams. • Iterate from problem framing to prototyping and deploying production-grade solutions. • Deliver reliable, scalable AI features in production. • Build prototypes and develop data pipelines for deployment. • Experiment with generative LLMs to create and enhance generative applications using prompts and user feedback. • Clearly communicate efforts and impact to both technical and business audiences.