Research Internship : Reliable and Adaptive Machine Learning

Sigma Nova is looking for a research intern (starting in Q1 / early Q2 2027) to investigate fundamental questions in machine learning: how can models adapt to new datasets, express reliable uncertainty, and learn from heterogeneous sources while respecting privacy?

This internship combines mathematical thinking with hands-on experimentation. You will work with researchers on methods that connect modern foundation models with ideas from probability, Bayesian inference, and optimal transport. The exact topic will be shaped around your interests and background, with a focused scope and opportunities to contribute to a publication.

Possible research tracks

1) Bayesian learning and uncertainty

Explore how models can provide useful uncertainty estimates alongside their predictions. Possible directions include scalable Bayesian approaches, parameter-efficient adaptation, and evaluating reliability when models encounter unfamiliar data.

2) Learning from distributed and heterogeneous data

Investigate how models can learn from multiple data sources without bringing all the data together. Topics may include federated learning, combining information across different populations, and understanding trade-offs between predictive performance, communication costs, and privacy.

3) Optimal transport and adaptation

Study how the geometry of probability distributions can help models compare datasets and transfer knowledge between tasks. Possible directions include learning compact dataset representations, selecting informative examples, and improving adaptation from limited observations.

These are alternative starting points rather than a single combined project. Depending on the topic, the balance may lean toward method development, mathematical analysis, or empirical evaluation.

• Master’s-level training in applied mathematics, statistics, machine learning, or a related field

• Strong Python skills and experience with PyTorch

• Solid foundations in probability, linear algebra, and optimization

• Comfortable reading research papers and designing careful, reproducible experiments

• Interest in Bayesian inference, optimal transport, or privacy-preserving learning

• Prior experience in one of these areas is a plus; expertise in all of them is not expected

What we offer

• A research-driven startup working on foundation models

• Close mentorship from researchers working on optimal transport and Bayesian deep learning

• High-end GPU resources and existing research code to build on

• Room to shape the research direction and contribute to a publication

1) Prescreen with Recruiter

2) Hiring meeting with one NeuroAI team member

3) Onsite interview (coding and research discussion)