Project Overview

We are hiring highly motivated and talented research engineer interns who are interested in advancing the state of the art in resource-efficient machine learning at the Singapore-MIT Alliance for Research & Technology (SMART). This position is part of the program: “Mens, Manus, and Machina: How AI empowers people and the city in Singapore (M3S).” 

As the field increasingly pivots toward foundation models, efficiency has become a central challenge. Addressing this challenge requires approaches that go beyond incremental optimization. We seek research engineer interns to contribute to the development of next-generation machine learning methods that fundamentally rethink how large-scale AI systems are trained, fine-tuned, and deployed. Our focus is on the development and application of advanced techniques, including AutoML, Bayesian optimization, neural architecture search, reinforcement learning, and active learning, with the explicit goal of achieving significant gains in computational efficiency without sacrificing performance.

Responsibilities

1) Contribute to the development of resource-efficient machine learning methods that improve the computational efficiency of large-scale AI systems, such as foundation models (e.g., LLMs, VLMs, Multimodal models) and World models, while maintaining or improving performance.

2) Design, prototype, implement, and rigorously evaluate complex machine learning systems to assess the performance, scalability, and practical feasibility of novel theoretical ideas. 

3) Collaborate closely with senior researchers and PhD students to publish research results in top-tier conferences and journals aligned with the above-mentioned areas. 

For more information on our research group and interests, visit:

Daniela Rus (MIT) https://danielarus.csail.mit.edu/

Armando Solar-Lezama (MIT) https://people.csail.mit.edu/asolar/

Bryan Kian Hsiang Low (NUS) https://www.comp.nus.edu.sg/~lowkh/index.html

Requirements

1) Strong programming proficiency, with demonstrated experience with large-scale deep learning models and modern ML frameworks (e.g., PyTorch, JAX, Transformers). 

2) Studies in a bachelor's/master's degree program in Computer Science, Machine Learning, Statistics, Mathematics, or a closely related quantitative discipline.   

The internship must be approved by the University.