Senior Machine Learning Engineer, Caper (Fraud)

Overview

The AI and CV team at Caper (Instacart) is dedicated to building state of the art solutions to help retailers around the world create seamless shopping experiences via our AI-driven smart cart. Our team of enthusiastic researchers and engineers are spread across different time zones, but collaborate effectively with each other on multiple exciting projects. 

As a Machine Learning Engineer at Caper, you will shape the core of our multi-modal anti-theft strategy by leveraging our sensor data to deeply understand the customer interactions and cart contents while making effortless experiences for our customers. You’ll work alongside our computer vision, hardware, and front end engineers, to bring together our various sensor inputs that create an intuitive and accurate experience on our shopping carts. You will shape how we store, use, and fuse, data and signals on a variety of complex topics, while leveraging a variety of multi-sensor data.

About the Job

In this role, you will:

• Drive end-to-end ML solutions from research to production
• Own the technical vision for our next generation anti-theft capabilities 
• Develop multimodal models to fuse the signals from our various sensors
• Implement sophisticated model architectures, training and evaluation pipelines
• Build infrastructure for collecting, processing and storing data
• Influence system design and product vision by making informed decisions

About You

Minimum Qualifications: 

• Master or Ph.D degree in ECE, Computer Science, Applied Maths or related fields.
• 3+ years experience in any of the following: sensor fusion, transformers, multi-modals, video understanding, and action/activity recognition
• Strong communication and presentation skills who can collaborate with diverse stakeholders 
• Strong programming skills with one or more of these languages: Python, C++, Java/Kotlin
• Strong skills in data science tools (SQL, Pandas,..), and machine learning frameworks (Pytorch, Tensorflow, Scikit-learn, ...)
• Proven records on driving end-to-end ML solutions from research to production

Preferred Qualifications

• Experience in designing and implementing end-to-end ML system that can fuse multi-modality data and understand/interpret a complex spatial-temporal problem 
• Knowledge of modern visual-language models and multi-modal AI systems
• Familiarity with A/B testing and experimentation methodologies in industrial settings
• Independent and self starter with the ability to identify and solve problems