Applied Artificial Intelligence/Machine Learning Scientist (Intern)

Our Applied AI/ML Science team builds and maintains the models that power the marketplace That work includes the shipping and delivery estimates retailers rely on as they shop and check out: what it will cost to ship and when it will arrive, before the order has been packed Within Applied Science, our Shipping and Fulfillment team builds the models behind those estimates, including models that learn to understand products from images and text Better predictions give retailers confidence in what they’re buying and, in turn, help brands sell more on Faire You’ll own a focused project in one of these areas: Predicting how an order will be packed: Before an order ships, we have to anticipate how it will be packed. You’ll build models that learn from what each item is and how items combine in a cart Recommending better ways to pack: How an order is packed shapes what it costs to ship. You’ll develop models that understand items from images and text and learn from historical packing outcomes to recommend packing that reduces shipping cost and improves efficiency Understanding products from a catalog: Listings often lack reliable weight, size and shape. You’ll train multimodal deep learning models that infer these physical characteristics from images, text and other catalog signals Whichever project you take on, you will: Survey the literature and existing approaches to identify promising ideas Prototype and train models offline, benchmarking against our current methods Build out the strongest approach into a working implementation Work with Applied Scientists and ML Engineers to test it against live traffic Present findings and recommendations to the team This paid Winter 2027 internship runs for 12 to 14 weeks, beginning in January 2027, with flexible start dates available for qualified candidates. Extensions may be offered based on project needs and mutual agreement Benefits Comprehensive healthcare: Including Health, Dental, Vision and Disability for all our locations. Time off: Paid time off, holidays and company-wide “Faire Fundays”. Parental leave: Generous parental and family leave, as well as fertility support benefits. Productivity support: Monthly stipends to help cover work from home connectivity needs. Annual learning grant: For personal and professional development, as well as unlimited access to training courses through LinkedIn Learning. Thoughtfully designed spaces: All of our offices have been designed with local in mind – from our architects to our coffee blends. Fitness and well-being benefits: Including monthly credit towards your wellness-related programmes. Mental health benefits: Including free access to Modern Health therapists and resources. Charitable matching: Faire will match up to £250 of your charity donations, every year. Career planning: Whether you want to grow as a leader, hone your craft or explore a new discipline, our career framework allows space for you to explore. Comfort working with noisy or incomplete real-world data Strong Python and SQL Currently enrolled in or recently graduated from a Master’s or PhD program in Computer Science, Machine Learning, Statistics, Electrical Engineering, or a related technical field A genuine enthusiasm for tackling ambiguous problems and learning new tools and techniques Hands-on experience building deep learning models (e.g., PyTorch), ideally with images, text, or both, including fine-tuning pre-trained models or working with embeddings Familiarity with gradient-boosted trees and other tabular ML methods Solid grounding in statistics and model evaluation, including benchmarking against strong baselines and estimating uncertainty Ability to read research papers and turn promising ideas into working code #J-18808-Ljbffr Faire