Job Description

Fixed-term: The funds for this post are available for 6 months.


We are seeking a Research Assistant (RA) to join the Cambridge Machine Learning Systems Lab (CaMLSys - https://mlsys.cst.cam.ac.uk) at the University of Cambridge's Department of Computer Science and Technology for 6 months in the first instance.


This position will be focused on developing and extending beyond SOTA methods for decentralized training in heterogeneous environments. It is part of the SPRIN-D Composite Challenge which is a joint effort between CaMLSys and Flower Labs, our mission is to build the next-generation federated AI infrastructure capable of operating across diverse devices and conditions. We will target privacy-preserving, fault-tolerant, and scalable training of all varieties.

Role Overview

We are seeking a motivated Research Engineer (formal title: Research Assistant) to support research and development in distributed training architectures. You will work closely with a world-class interdisciplinary team to implement and evaluate components such as decentralized orchestration, fault-tolerant training, and privacy-enhancing technologies.

Qualifications/Skills

Bachelor's or Master's degree in Computer Science, Machine Learning or related field, or equivalent industry experience Programming proficiency in Python and experience with ML frameworks (e.g., PyTorch, TensorFlow). Solid understanding of distributed systems or federated learning Strong communication and interpersonal skills Familiarity with MLOps tools Knowledge of privacy-preserving ML techniques Exposure to large-scale system designs or cloud/edge ML systems Prior experience of Flower or similar FL platforms

Responsibilities

Assist in the development and benchmarking of federated learning algorithms Contribute to MLOps tools for monitoring and scheduling in distributed setups Run experiments on heterogeneous hardware and summarize insights Participate in privacy-focused initiatives such as adaptive differential privacy (ADP-GC) and encryption methods (e.g., homomorphic encryption) Collaborate on publishing research results and technical documentation
The ideal candidate will be self-motivated, solutions-oriented, and have a solid understanding of FL. This is a unique opportunity to work with leading experts and pioneers in FL on an ambitious and impactful project.


Applicants should contact Prof Nicholas Lane for further information. https://mlsys.cst.cam.ac.uk/


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Please quote reference NR45865 on your application and in any correspondence about this vacancy.


The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.


The University has a responsibility to ensure that all employees are eligible to live and work in the UK. Please note that we provide the support of applying for the relevant visa (if required) and we reimburse the cost of the first visa.