Researcher Work From Home jobs

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, ensuring efficient use of GPU hardware for high-performance computing. • Develop and fine-tune GPU-accelerated machine learning solutions that power LinkedIn's large-scale AI applications, including recommendations, personalization, and search. • Collaborate with engineering and AI research teams to implement CUDA optimizations in existing models, enhancing both training and inference speeds. • Conduct research on GPU optimization techniques and apply them to complex machine learning 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 technologists to conduct independent research and help deliver intuitive solutions to our products and services 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 to the program after the completion of the internship • Experience with CUDA programming, GPU architectures, and parallel computing techniques. • Experience in deep learning frameworks like TensorFlow, PyTorch, and familiarity with their GPU implementations. • Understanding of machine learning and AI algorithms, with hands-on experience in model training and optimization. Preferred Qualifications: • Experience in optimizing large-scale deep learning models for GPU environments, focusing on both training and inference phases. • Experience with NVIDIA GPUs, cu DNN, 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/ML conferences (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 ************************************** Equal Opportunity Statement LinkedIn 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. 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