Nu AI Core Summer PhD Research Internship

Nubank•Palo Alto, CA

1d•Remote

About The Position

The Nu AI Core Summer Internship Program is a research-focused internship for PhD students working at the frontier of artificial intelligence and machine learning. Over approximately 12 weeks, interns collaborate with engineers, applied scientists, and researchers on complex AI challenges. Each intern leads an end-to-end research or applied AI project that combines research methods with large-scale production environments. The program aims to build long-term relationships between industry and academia and emerging AI research talent. Interns receive a dedicated research mentor, ownership of a scoped AI research project, and access to large-scale datasets and compute infrastructure. The program includes research talks and technical seminars, and concludes with a final research presentation to leadership and AI teams. Potential research areas include machine learning and deep learning, large language models and generative AI, recommender systems and ranking models, AI infrastructure and model optimization, and responsible AI and model evaluation. Projects typically combine research experimentation with applied system development, including model design, experimentation, and large-scale evaluation. The internship lasts approximately 12 weeks, with start dates from May to June aligned with US university calendars. The position is remote for US PhD students, with one fully covered in-person week at Nu’s Palo Alto office in Silicon Valley.

Requirements

Currently enrolled in a PhD program.
Research focus in machine learning, AI, statistics, or a related area.
Strong programming skills in Python or similar languages.
Experience working with machine learning frameworks such as PyTorch or TensorFlow.
Experience designing experiments and evaluating models.
Experience working with large datasets or training machine learning models.

Nice To Haves

Publications at top AI/ML conferences such as NeurIPS, ICML, ICLR, ACL, KDD, RecSys, CVPR, or CIKM, or equivalent conferences in related fields.
Experience working with large-scale machine learning systems or distributed training.
Experience with LLMs, generative AI, or foundation models.
Open-source contributions in AI or machine learning.
Experience collaborating in interdisciplinary research environments.
Strong research rigor and experimental methodology.
Ability to translate research ideas into practical systems.
Track record of identifying novel research directions or reframing existing problems.
Intellectual curiosity and independent problem-solving ability.
Interest in deploying AI systems with real-world impact.

Responsibilities

Lead an end-to-end research or applied AI project.
Collaborate with engineers, applied scientists, and researchers on complex AI challenges.
Combine research experimentation with applied system development, including model design and large-scale evaluation.
Present research findings to leadership and AI teams.