Crossing Minds is an artificial intelligence company founded in 2017 by AI researchers Alexandre Robicquet, Dr. Emile Contal, and Google X founder Dr. Sebastian Thrun. While researching together, they realized there was a more thoughtful way to make recommendations.
While our founding team includes world-renowned AI pioneers and experts, our company exists because we believe that the latest advancements in machine learning shouldn’t be locked up in academia. Instead, they should be accessible through safe and concrete products built for everyone.
Crossing Minds raised $10 million in October 2021 and is the only AI recommender system backed by Shopify. Our customer roster includes companies such as Brut, Chanel, Camp, and Reelgood. We are now in a new growth stage with a team of 30 members and are excited to continue developing talent in our San Francisco, Paris, and Toronto offices.
We seek an applied machine learning engineer intern to join Crossing Minds’ Paris office.
As part of the applied machine learning engineering team at Crossing Minds, you will work with other engineers on the team to unlock the value of recommender systems for our customers. You will develop solutions to challenging machine learning problems by exploring datasets, training models, and delivering great recommendations to our customers. You will contribute to directly impacting not only the business success of our customers but also millions of people around the world in their discovery of items better tailored to their tastes.
Applied machine learning engineers are also critical contributors to the evolution of our core recommendation platform. Each customer, each dataset, and each model offers a unique learning opportunity to improve our tech stack, allowing us to deliver the best recommendations with the least effort.
What you should have
Proficiency in Python, NumPy, and pandas librariesData-oriented problem-solving skills: exploration, visualization, features extraction, and model tuningUnderstanding Machine Learning models: objective functions, regularization, gradient descent, optimization techniques, the curse of dimensionality, etc.ML feature engineering (e.g., tokenization, pre-processing and aggregation, pre-trained embeddings)
Optional
Experience with recommender systems in the form of an internship, course, or personal projectExperience with software development practices, including git proficiency, design patterns, and contributing to large code basesExperience as a Machine Learning Engineer in a professional settingExperience in customer-facing roles
