About the Role

This is a PhD-level research scientist internship focused on advancing AI and machine learning through core algorithmic development. Interns will work on state-of-the-art ML algorithms, collaborate with researchers, and contribute to Meta's product development. The internship lasts 12-24 weeks with flexible start dates.

Key Responsibilities

• Develop novel state-of-the-art machine learning algorithms and systems using deep learning techniques
• Analyze and improve efficiency, scalability, and stability of deployed algorithms
• Perform state-of-the-art research to advance Machine Learning and Artificial Intelligence science
• Devise better data-driven models for information retrieval, multi-modal fusion, generation, or media understanding
• Collaborate with researchers and cross-functional partners on research plans and results

Required Skills & Qualifications

Must Have:

• Currently has or is in process of obtaining PhD in Machine Learning, AI, Computer Science, or related technical field
• Must obtain work authorization in country of employment at time of hire
• Experience with Python, C++, C, Java or other related programming language
• Experience with deep learning frameworks such as Pytorch or Tensorflow
• Experience building systems based on machine learning and/or deep learning methods
• Research experience with algorithms for sequential decision-making (planning, reinforcement learning, or similar)

Nice to Have:

• Intent to return to degree program after internship completion
• Proven track record demonstrated by grants, fellowships, patents, or first-authored publications at leading conferences
• Demonstrated experience and self-driven motivation in solving analytical problems using quantitative approaches
• ML/AI research/work experience in information retrieval, generative approaches, NLP, CV, or Speech/Audio
• Demonstrated software engineer experience via internships, work experience, coding competitions, or open source contributions
• Experience working and communicating cross functionally in a team environment