Research Scientist Intern, AI Alignment

Meta is committed to advancing the field of artificial intelligence by making fundamental advances in technologies to help interact with and understand our world. We seek individuals passionate about deep learning, computer vision, optimization, natural language processing, machine learning, reinforcement learning, computational statistics, applied mathematics, and security/privacy. Our interns have an opportunity to make core algorithmic advances and apply their ideas at an unprecedented scale.

Our internships are 12 to 16 weeks, or 24 weeks long, with various start dates throughout the year.

Responsibilities

• Develop novel state-of-the-art algorithms and corresponding systems, leveraging various deep learning techniques
• Analyze and improve various aspects of the corresponding algorithms and systems, including efficiency, scalability, stability, fairness, security, and privacy
• Perform state-of-the-art research to advance the science and technology of machine learning and artificial intelligence
• Collaborate with researchers and cross-functional partners, including communicating research plans, progress, and results
• Publish research results and contribute to research that can be applied to Meta product development

Qualifications

• Currently has or is pursuing a Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or a relevant technical field
• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment
• Experience with Python, C++, C, Java, or other related languages
• Experience with deep learning frameworks such as PyTorch or TensorFlow
• Intent to return to degree program after completion of the internship/co-op
• Proven track record of significant results demonstrated by grants, fellowships, patents, and publications at leading workshops or conferences (e.g., NeurIPS, ICLR, AAAI, RecSys, KDD, IJCAI, CVPR, ECCV, ACL, NAACL, EACL, ICASSP, CCS, IEEE S&P, MLSys, or similar)
• Demonstrated experience and self-driven motivation in solving analytical problems using quantitative approaches
• Experience building large-scale machine learning systems and training with large datasets
• Experience communicating complex research clearly, precisely, and actionably
• Demonstrated software engineering experience via an internship, work experience, coding competitions, or widely used contributions in open-source repositories (e.g., GitHub)
• Experience working and communicating cross-functionally in a team environment