LinkedIn is the world's largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We're also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that's built on trust, care, inclusion, and fun - where everyone can succeed.?
Join us to transform the way the world works.
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
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
Basic Qualifications:
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
Preferred Qualifications:
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
Suggested Skills:
Machine learning and deep learning
Advanced data mining
Strategic thinking and problem-solving capabilities
LinkedIn is committed to fair and equitable compensation practices.
The pay range for this role is $62 to $75 per hour. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor.
The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans. For more information, visit https://careers.linkedin.com/benefits .
Equal Opportunity Statement
We seek candidates with a wide range of perspectives and backgrounds and we are proud to be an equal opportunity employer. LinkedIn considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class.
LinkedIn is committed to offering an inclusive and accessible experience for all job seekers, including individuals with disabilities. Our goal is to foster an inclusive and accessible workplace where everyone has the opportunity to be successful.
If you need a reasonable accommodation to search for a job opening, apply for a position, or participate in the interview process, connect with us at accommodations@linkedin.com and describe the specific accommodation requested for a disability-related limitation.
Reasonable accommodations are modifications or adjustments to the application or hiring process that would enable you to fully participate in that process. Examples of reasonable accommodations include but are not limited to:
Documents in alternate formats or read aloud to you
Having interviews in an accessible location
Being accompanied by a service dog
Having a sign language interpreter present for the interview
A request for an accommodation will be responded to within three business days. However, non-disability related requests, such as following up on an application, will not receive a response.
LinkedIn will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by LinkedIn, or (c) consistent with LinkedIn's legal duty to furnish information.
San Francisco Fair Chance Ordinance ?
Pursuant to the San Francisco Fair Chance Ordinance, LinkedIn will consider for employment qualified applicants with arrest and conviction records.
Pay Transparency Policy Statement ?
As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: https://lnkd.in/paytransparency.
Global Data Privacy Notice for Job Candidates ?
Please follow this link to access the document that provides transparency around the way in which LinkedIn handles personal data of employees and job applicants: https://legal.linkedin.com/candidate-portal.
