Seldon is looking for an Applied Machine Learning Intern to join our team. We are focused on making it easy for machine learning models to be deployed and managed at scale in production. We provide Cloud Native products that run on top of Kubernetes and are open-core with several successful open source projects including Seldon Core, Alibi:Explain and Alibi:Detect. Within our Data Science team we work on distilling and conducting research to enable trust in production machine learning systems.
About the role
- Conduct applied research in the areas of model explainability, outlier and drift detection, model monitoring and more.
- Working towards a degree or higher level degree in a numerical subject.
- A solid foundation in machine learning, including probability and statistics.
- Python programming experience.
- A critical approach to reviewing existing academic literature and research software.
- Ability to formulate experiment designs and comprehensively test hypotheses.
About our tech stack
Some of our technical projects:
- We built and maintain the model explainability library Alibi:Explain, and the outlier, drift and adversarial detection toolkit Alibi:Detect.
- We are core authors and maintainers of Seldon Core, the most popular Open Source model serving solution in the Cloud Native (Kubernetes) ecosystem
- We run the largest Tensorflow meetup in London
- And much more!
- Remote working available.
- Exciting phase of fast-paced start-up challenges with an ambitious team and unlimited potential for professional growth.
Our interview process is normally a phone interview and 2-3 hours of final interviews (carried out virtually). We promise not to ask you any brain teasers or trick questions. We might discuss papers or design an algorithm, the same way we often work together, but we won’t make you write code on the spot. Our recruitment process has an average length of 3 weeks.