Nu serves more than 140 million customers, guided by a mission to fight complexity and empower people. The company has been leading an industry transformation through innovative products and human-centered services.
Proprietary technology and data at scale power Nu’s digital platform, built to promote financial access, advancement, and transparency. Its business model thrives on customer love and lower costs, feeding a flywheel of growth and profitability.
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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 about 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 builds long-term relationships between industry and academia and with emerging AI research talent.
Duration: ~12 weeks
Start dates: May–June, aligned with US university calendars
Location model: Remote, with one in-person week at Nu's Palo Alto office in Silicon Valley, fully covered by us.
What you get
Dedicated research mentor
Ownership of a scoped AI research project
Access to large-scale datasets and compute infrastructure
Research talks and technical seminars
Final research presentation to leadership and AI teams
Interns may work on projects related to:
Machine learning and deep learning
Large language models and generative AI
Recommender systems and ranking models
AI infrastructure and model optimization
Responsible AI and model evaluation
Projects typically combine research experimentation with applied system development, including model design, experimentation, and large-scale evaluation.
This program targets PhD students conducting research in AI and machine learning who are interested in applying their work to real-world systems.
Applicants are typically pursuing a PhD in fields such as:
Computer Science
Machine Learning
Artificial Intelligence
Statistics
Mathematics
Computational Linguistics
Electrical Engineering
or related quantitative disciplines.
Currently enrolled in a PhD program
Research focus in machine learning, AI, statistics or related areas
Strong programming skills in Python or similar languages
Experience working with machine learning frameworks (e.g., PyTorch, TensorFlow)
Experience designing experiments and evaluating models
Experience working with large datasets or training ML models
Strong candidates may also demonstrate:
Publications at top AI/ML conferences (e.g., NeurIPS, ICML, ICLR, ACL, KDD, RecSys, CVPR, CIKM or equivalent-conferences for related fields)
Experience working with large-scale ML systems or distributed training
Experience with LLMs, generative AI, or foundation models
Open-source contributions in AI or ML
Experience collaborating in interdisciplinary research environments
We are particularly interested in candidates who demonstrate:
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
Collaboration with engineers and cross-functional teams
Interest in deploying AI systems with real-world impact
Top Tier Medical Insurance
Top Tier Dental and Vision Insurance
Life Insurance and AD&D
Extended maternity and paternity leaves
Nucleo - Our learning platform of courses
NuLanguage - Our language learning program
NuCare - Our mental health and wellness assistance program
401K
Saving Plans - Health Saving Account and Flexible Spending Account
Remote, but only for USA PhD students
Our recruitment process may involve the use of artificial intelligence–enabled tools, such as automated interview transcription and analysis, to support the evaluation process. Artificial intelligence is not used to make final hiring decisions; all decisions are made by human reviewers.
To maintain a consistent and fair process for every candidate, Nu does not provide individualized technical feedback. See how our policy works here
Conduct independent and collaborative AI research, designing large-scale deep learning experiments and developing models for high-stakes applications in medicine, finance, and law.
Research human-aware robot learning and human-robot interaction, developing and testing data-driven policies that support reliable collaboration between people and robots in real-world tasks.
Design therapeutic proteins through computational campaigns that integrate machine learning, biochemistry, and experimental data, while translating program learnings into reusable workflows and clear design recommendations.
Develop computational structural biology methods and sequence datasets to predict protein-protein interactions in host-pathogen systems, model protein structures, and identify novel biological systems.
We have an opening for a Postdoctoral Researcher in the areas of atomistic and multiscale modeling to optimize metal joining in alloyed systems.
