GenAI Research Scientist Intern

Company Description: Founded in late 2020 by a group of machine learning engineers and researchers, MosaicML allows companies to securely fine-tune, train and deploy custom AI models on their own data, ensuring maximum security and control. The MosaicML platform, compatible with all major cloud providers, offers utmost flexibility for AI development. In 2023, MosaicML introduced its pretrained transformer models, setting a new standard for open source, commercially usable LLMs and receiving over 3 million downloads. MosaicML firmly believes that a company's AI models are as valuable as any other core IP, and that high-quality AI models should be accessible to all. Since becoming part of Databricks in July 2023, we are dedicated to enabling our customers to tackle the world's most challenging problems, from realizing the next mode of transportation to accelerating the development of medical breakthroughs. We accomplish this by building and operating the world's premier data and AI platform, allowing our customers to leverage deep data insights to enhance their business. We eagerly tackle technical challenges, aiming to provide our customers with superior data and AI tools. Job Description: You will collaborate with one or more researchers on a project aimed at enhancing our existing projects to make neural network training more efficient. This may include: - Adapting, improving, and assessing a method from the literature. - Creating a completely new method. - Combining multiple methods to develop new protocols for efficient training. - Scientifically investigating how neural networks learn in real-world settings. - Exploring new strategies for training neural networks. Your qualifications and qualities: Required: - Currently pursuing an undergraduate or graduate degree in computer science or related fields (electrical engineering, neuroscience, physics, math, etc.). - Fundamental knowledge of deep learning. - Advanced software engineering skills, including proficiency with PyTorch. Nice to have: - Understanding of the system aspects of how neural networks train and the resources used during the process. - Prior research experience in deep learning. Pay Range Transparency Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is detailed below and represents base salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are influenced by several uniquely individual factors like job-related skills, depth of experience, relevant certifications, training and specific work location. Based on these factors, Databricks utilizes the full width of the range. The total compensation package for this position may also include eligibility for an annual performance bonus, equity and the benefits listed above. For more information regarding which range your location is in, visit our page here.