NubankNu AI Core Summer PhD Research Internship
Nubank is hiring a Nu AI Core Summer PhD Research Internship for a remote role open to applicants in United States. Level rates it ; you can apply on Level.
Level's read on this job
- Builds AI ●●●● is how Level rates this role: the job is building AI systems.
- This is listed as a remote role.
- The posting mentions PyTorch, TensorFlow and AI evaluation among its tools and skills.
- Nubank has 97 other open roles on Level.
- This posting went up in the last few days.
AI in this role
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.
Visit our Institutional Page
About the Program
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.
Program Structure
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.
Candidate Profile
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.
Basic Qualifications (Application/ Screening Filters)
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
What We Value
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
Location for this opportunity
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
How we rate this
Nu AI Core Summer PhD Research Internship at Nubank rates 99 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How do you decide that one model's output is better than another's for a given task?
- How do you think about the risk of an AI system in this kind of role failing silently?
- Tell me about a research question you investigated. What did you find?
- What's a project where you used PyTorch hands-on?
- Walk me through how you've used TensorFlow in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: AI Evaluation, AI Safety, AI Research, PyTorch, and TensorFlow. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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