AI Engineering Intern - Summer 2027

7 days ago

Santa Monica, CA, USAIntern

Responsibilities

Assist in building and deploying agentic frameworks, RAG pipelines, retrieval architectures, and automated workflows for structured data analysis.
Contribute to small language model tuning, computer vision, LLM application development, personalization, search, and recommendation initiatives.
Help design and develop scalable frontend and backend solutions with test automation and code-quality practices.
Optimize ML models, data pipelines, retrieval systems, and deployment configurations for production performance, reliability, and resource efficiency.
Build applications and data workflows on Palantir Foundry/AIP, including ontology modeling and AIP-driven agentic workflows.
Support cloud engineering projects and the development, deployment, and maintenance of scalable AI/ML reference architectures and platforms.
Collaborate with research, engineering, product, program management, data engineering, data science, and business teams to integrate AI/ML innovations and resolve system issues.
Support generative AI and personalized LLM integration into products and services while contributing to AI/ML roadmaps and stakeholder partnerships.

Bachelor’s degree in computer science, engineering, data science, or a related field, or equivalent practical experience.
Experience in AI engineering focused on developing and deploying machine learning models and cloud-based AI/ML solutions.
Experience leading or contributing to the integration, design, and optimization of scalable ML models, data pipelines, and cloud infrastructure for AI applications.
Experience collaborating with cross-functional teams and business leaders on strategic AI initiatives.
Strong cloud engineering expertise for ML training platforms, deployment, and generative AI solutions using cloud-based models.
Preferred: master’s degree in computer science, engineering, data science, or a related field, or equivalent practical experience.
Preferred: experience developing and deploying LLMs and generative AI solutions, and familiarity with RAG or retrieval pipelines, agentic workflows such as LangGraph, and cloud AI scalability, performance, and security.
Preferred: applied research experience in ML and LLM techniques, AI/ML or data governance knowledge, and experience integrating AI breakthroughs into products.

51-200 employees