Posted on:
November 5, 2024
What we’re building and why we’re building it. Every month, millions of people use America’s Rewards App, earning rewards for buying brands they love – and a whole lot more. Fetch empowers consumers to live rewarded throughout their day, whether shopping in the grocery aisle, grabbing a bite at the drive-through, or playing a favorite mobile game. To date, we’ve delivered more than $1 billion in rewards and earned over 5 million five-star reviews from happy users. Our investors include SoftBank, Univision, and Hamilton Lane, in addition to partnerships with both challenger brands and Fortune 500 companies, all contributing to our mission of reshaping how brands and consumers connect in the marketplace. At Fetch, user and partner success are central to our operations, and we extend that same commitment to our employees. Ranked as one of America’s Best Startup Employers by Forbes for two consecutive years, Fetch fosters a people-first culture rooted in trust, accountability, and innovation, encouraging our employees to challenge ideas, think bigger, and bring fun to Fetch. Fetch is an equal employment opportunity employer.
The ML Engineering team embodies these values and focuses on enabling 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 join an exciting company in a high-growth phase. Fetch utilizes ML/AI for various applications such as receipt understanding (digital and physical), fraud detection, and evolving areas like ads ranking, recommendation, search, and discovery. Your role will center on developing ML models and the infrastructure necessary to operationalize them at scale, covering the full life-cycle of machine learning, including data management, model development, and deployment in large-scale production settings. Collaborating closely with backend, DevOps, and data scientists, you will create value in a fast-moving environment.
Are you capable of training and deploying a Transformer model but know when a simpler solution will do? Do you enjoy understanding how model architectures translate to flops and the milliseconds off a server? Have you spent entire days debugging inscrutable CUDA errors? If so, we’d love to hear from you. This is not a college internship, and applicants should have graduated by June 2024.
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