Senior Machine Learning Engineer

What we’re building and why we’re building it. 

There’s a reason Fetch is ranked top 10 in Shopping in the App Store. Every day, millions of people earn Fetch Points buying brands they love. From the grocery aisle to the drive-through, Fetch makes saving money fun. We’re more than just a build-first tech unicorn. We’re a revolutionary shopping platform where brands and consumers come together for a loyalty-driving, points-exploding, money-saving party.

Join a fast-growing, founder-led technology company that’s still only in its early innings. Ranked one of America’s Best Startup Employers by Forbes two years in a row, Fetch is building a people-first culture rooted in trust and accountability. How do we do it? By empowering employees to think big, challenge ideas, and find new ways to bring the fun to Fetch. So what are you waiting for? Apply to join our rocketship today!

Fetch is an equal employment opportunity employer.

About the Role: 

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 Senior Machine Learning Engineer to contribute to this vision and reap the rewards of joining an exciting company in the high growth phase. Among other things, Fetch uses ML/AI to power receipt understanding (digital and physical), fraud detection, and we’re rapidly evolving in the space of ads ranking, recommendation, search, and discovery. 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 developing ML models and the infrastructure needed to operationalize them at scale. The ML Team at Fetch is responsible for the full life-cycle of machine learning. This includes data labeling/collection/management, model development, and deploying to large scale production settings. We work closely with backend, devops and data scientists on focussed teams. 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 …