Analytical and Problem solving, Apache Kafka, AWS, CUDA kernels, Effective communication skills, Machine learning techniques, Python Programming, PyTorch, TensorFlow
The ML Engineering team embodies these values and works with a laser focused objective to enable intelligent systems for end users, internal stakeholders, and external partners. We are looking for a Machine Learning Engineer Apprentice to contribute to this vision and reap the rewards of joining an exciting company in the high growth phase. Among other things, Fetch uses multiple ML models to power every scan in the app (millions a day and growing), fight against fraudulent behavior, and drive recommendations for users. Machine learning is core to our product and we’re working to make it an even bigger part of the company.
Your focus will be on the intersection of training ML models and deploying them to production. MLE’s at Fetch are responsible for the full cycle of machine learning on a team. This includes managing/cleaning/piping data, training models for iterative improvements, and deploying those models to production. This will be done in collaboration with backend engineers and data scientists on a product team. You’ll be expected to create value in a fast moving environment and that might mean at any given moment deep diving into one of these stages of the pipeline.
Are you capable of training and deploying a Transformer model but know when a simpler solution will do? Do you like knowing how model architectures translate to flops and the milliseconds off a server? Have you lost entire days debugging inscrutable CUDA errors? If you answered yes to these questions we’d love to hear from you.
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Fetch is America’s leading consumer-engagement platform that rewards shoppers for buying the brands they love. The Fetch app gives users the easiest way to save on everyday purchases by simply scanning their receipt. For our brand partners, the platform allows them to understand a 360 degree view of shopping habits, and to meaningfully reward a customer's individual loyalty.