AI/ML Engineer Intern - Generative AI, PhD - Summer 2026 (Mountain View, CA)
About The Position
This internship role will be based out of Mountain View, CA. At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team. LinkedIn is seeking innovative and motivated PhD students to join our team as Generative AI Engineering Interns. As a part of our AI/ML teams, you will work on advancing the frontier of Generative AI, applying cutting-edge techniques in areas such as text generation, image synthesis, multimodal models, evaluation frameworks, and reinforcement learning. You'll collaborate with a dynamic group of AI researchers and engineers to develop scalable, production-ready models that impact LinkedIn's products and user experiences. LinkedIn's Machine Learning Engineers are both data/research scientists and software engineers, who develop and implement machine learning models and algorithms. Unlike other companies that separate these roles, our engineers work on projects from ideation to implementation. Our mission is crystal clear: to elevate the LinkedIn member experience through the implementation of cutting-edge technologies that enable advanced cognitive understanding of multimedia content. Whether it's text, images, videos, ads, or live content, we are leading the way in developing state-of-the-art large vision language technologies. Candidates must be currently enrolled in a PhD program, with an expected graduation date of December 2026 or later. Our internships are 12 weeks in length and will have the option of two intern sessions: May 26th, 2026 - August 14th, 2026 June 15th, 2026 - September 4th, 2026
Requirements
- Currently pursuing a PhD in computer science, statistics, mathematics, electrical engineering, machine learning, or related technical field and returning to the program after the completion of the internship
- Proven research experience in Generative AI, including LLMs, GANs, VAEs, diffusion models, or similar architectures
- Knowledge of generative models, neural networks, and probabilistic methods for AI
- Proven experience with programming languages such as Python and machine learning libraries like TensorFlow or PyTorch
Nice To Haves
- Proven track record in developing machine learning algorithms for solving computer vision and graphics problems (e.g., generative models for images and videos), as well as prototyping invented algorithms
- Experience with multimodal learning, combining visual and textual data in Generative AI systems
- Knowledge of reinforcement learning applied to Generative AI tasks
- Hands-on experience deploying generative models in production environments
- Publication record in AI/ML conferences (e.g. NeurIPS, ICML, CVPR, ICCV)
- Proficiency in Python and deep learning frameworks (e.g. PyTorch, TensorFlow, JAX)
- Involvement in consumer-facing product development and design
- Understanding of configuration management techniques and tools
- Proven proficiency with command of algorithms and data structures
- Excellent communication skills
Responsibilities
- Conduct research and development on state-of-the-art Generative AI models, including transformers, diffusion models, GANs, and autoregressive architectures
- Apply advanced Generative AI techniques to a variety of tasks such as text generation, creative content generation, conversational agents, and multimodal learning
- Develop and implement large-scale, production-quality Generative AI systems that integrate with LinkedIn's platform
- Design and implement evaluation frameworks for AI models, including metrics, datasets, and pipelines for automated testing and benchmarking
- Collaborate with product teams to build innovative AI-driven user experiences, from personalized content to conversational agents
- Contribute to internal frameworks for human-in-the-loop annotation and preference modeling
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What This Job Offers
Career Level
Intern
Industry
Administrative and Support Services
Education Level
Ph.D. or professional degree
Number of Employees
5,001-10,000 employees
