LinkedIn
PHD AI- Optimization (CUDA) Summer 2025
LinkedIn, Mountain View, California, us, 94035
LinkedIn was built to help professionals achieve more in their careers, and everyday millions of people use our products to make connections, discover opportunities, and gain insights. Our global reach means we get to make a direct impact on the world’s workforce in ways no other company can. We’re much more than a digital resume – we transform lives through innovative products and technology.
We are looking for Artificial Intelligence interns to work on our massive semi-structured text, graph and user activity data sets. This internship will specialize in Optimization. The role will focus on optimizing AI models and algorithms for performance and efficiency on GPU hardware, enabling LinkedIn to scale and enhance its machine learning applications. As part of this team, you’ll work with deep learning frameworks and help drive innovation in high-performance AI systems.
Candidates must be currently enrolled in a PhD program, with an expected graduation date of December 2025 or later.
At LinkedIn, we trust each other to do our best work where it works best for us and our teams. This role offers a hybrid work option, meaning you can both work from home and commute to a LinkedIn office, depending on what’s best for you and when it is important for your team to be together. Our internship roles will be based in Mountain View, CA; or other US office locations.
Our internships are 12 weeks in length and will have the option of two intern sessions* May 27th, 2025 - August 15th, 2025* June 16th, 2025 - September 5th, 2025
Responsibilities
Optimize and accelerate machine learning algorithms and AI models using CUDA, ensuringefficient use of GPU hardware for high-performance computing.
Develop and fine-tune GPU-accelerated machine learning solutions that power LinkedIn’slarge-scale AI applications, including recommendations, personalization, and search.
Collaborate with engineering and AI research teams to implement CUDA optimizations inexisting models, enhancing both training and inference speeds.
Conduct research on GPU optimization techniques and apply them to complex machinelearning challenges, including deep learning architectures and large-scale data processing.
Stay updated with the latest developments in GPU computing, deep learning frameworks(e.g., TensorFlow, PyTorch), and parallel programming to continuously improve performance.
Work in a highly collaborative environment with mentors, business experts and technologiststo conduct independent research and help deliver intuitive solutions to our products andservices
Basic Qualifications
Currently pursuing a PhD in computer science, statistics, mathematics, electrical engineering,machine learning, with a focus on GPU optimization, or related technical field and returning tothe program after the completion of the internship
Experience with CUDA programming, GPU architectures, and parallel computingtechniques.
Experience
in deep learning frameworks like TensorFlow, PyTorch, and familiarity with theirGPU implementations.
Understanding of machine learning and AI algorithms, with hands-on experience inmodel training and optimization.
Preferred Qualifications
Experience in optimizing large-scale deep learning models for GPU environments, focusingon both training and inference phases.
Experience with NVIDIA GPUs, cuDNN, or other GPU libraries and tools.
Problem-solving skills and ability to work on complex AI optimization challenges in a fast-paced environment.
Publication record in high-performance computing, GPU optimization, or related AI/MLconferences (e.g., NeurIPS, ICML, SC).
Hands-on experience with deploying and scaling GPU-accelerated models in production.
Experience
in Python, and experience with machine learning and deep learning toolkits (e.g.,TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)
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 $57 - $70 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 StatementLinkedIn is committed to diversity in its workforce and is 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 an Affirmative Action and Equal Opportunity Employer as described in our equal opportunity statement here: https://microsoft.sharepoint.com/:b:/t/LinkedInGCI/EeE8sk7CTIdFmEp9ONzFOTEBM62TPrWLMHs4J1C_QxVTbg?e=5hfhpE. Please reference https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf and https://www.dol.gov/ofccp/regs/compliance/posters/pdf/OFCCP_EEO_Supplement_Final_JRF_QA_508c.pdf for more information.
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.
Pay Transparency Policy StatementAs 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 CandidatesThis document provides transparency around the way in which LinkedIn handles personal data of employees and job applicants: https://lnkd.in/GlobalDataPrivacyNotice
We are looking for Artificial Intelligence interns to work on our massive semi-structured text, graph and user activity data sets. This internship will specialize in Optimization. The role will focus on optimizing AI models and algorithms for performance and efficiency on GPU hardware, enabling LinkedIn to scale and enhance its machine learning applications. As part of this team, you’ll work with deep learning frameworks and help drive innovation in high-performance AI systems.
Candidates must be currently enrolled in a PhD program, with an expected graduation date of December 2025 or later.
At LinkedIn, we trust each other to do our best work where it works best for us and our teams. This role offers a hybrid work option, meaning you can both work from home and commute to a LinkedIn office, depending on what’s best for you and when it is important for your team to be together. Our internship roles will be based in Mountain View, CA; or other US office locations.
Our internships are 12 weeks in length and will have the option of two intern sessions* May 27th, 2025 - August 15th, 2025* June 16th, 2025 - September 5th, 2025
Responsibilities
Optimize and accelerate machine learning algorithms and AI models using CUDA, ensuringefficient use of GPU hardware for high-performance computing.
Develop and fine-tune GPU-accelerated machine learning solutions that power LinkedIn’slarge-scale AI applications, including recommendations, personalization, and search.
Collaborate with engineering and AI research teams to implement CUDA optimizations inexisting models, enhancing both training and inference speeds.
Conduct research on GPU optimization techniques and apply them to complex machinelearning challenges, including deep learning architectures and large-scale data processing.
Stay updated with the latest developments in GPU computing, deep learning frameworks(e.g., TensorFlow, PyTorch), and parallel programming to continuously improve performance.
Work in a highly collaborative environment with mentors, business experts and technologiststo conduct independent research and help deliver intuitive solutions to our products andservices
Basic Qualifications
Currently pursuing a PhD in computer science, statistics, mathematics, electrical engineering,machine learning, with a focus on GPU optimization, or related technical field and returning tothe program after the completion of the internship
Experience with CUDA programming, GPU architectures, and parallel computingtechniques.
Experience
in deep learning frameworks like TensorFlow, PyTorch, and familiarity with theirGPU implementations.
Understanding of machine learning and AI algorithms, with hands-on experience inmodel training and optimization.
Preferred Qualifications
Experience in optimizing large-scale deep learning models for GPU environments, focusingon both training and inference phases.
Experience with NVIDIA GPUs, cuDNN, or other GPU libraries and tools.
Problem-solving skills and ability to work on complex AI optimization challenges in a fast-paced environment.
Publication record in high-performance computing, GPU optimization, or related AI/MLconferences (e.g., NeurIPS, ICML, SC).
Hands-on experience with deploying and scaling GPU-accelerated models in production.
Experience
in Python, and experience with machine learning and deep learning toolkits (e.g.,TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)
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 $57 - $70 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 StatementLinkedIn is committed to diversity in its workforce and is 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 an Affirmative Action and Equal Opportunity Employer as described in our equal opportunity statement here: https://microsoft.sharepoint.com/:b:/t/LinkedInGCI/EeE8sk7CTIdFmEp9ONzFOTEBM62TPrWLMHs4J1C_QxVTbg?e=5hfhpE. Please reference https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf and https://www.dol.gov/ofccp/regs/compliance/posters/pdf/OFCCP_EEO_Supplement_Final_JRF_QA_508c.pdf for more information.
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.
Pay Transparency Policy StatementAs 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 CandidatesThis document provides transparency around the way in which LinkedIn handles personal data of employees and job applicants: https://lnkd.in/GlobalDataPrivacyNotice