Machine Learning Engineer

Job expired!

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 purchasing brands they love. From the grocery lane to the drive-through, Fetch makes saving money entertaining. We’re more than just a build-first tech unicorn. We’re a transformative shopping platform where brands and consumers congregate for a loyalty-enhancing, points-boosting, cost-cutting party.

Join a rapidly expanding, founder-led technology company that’s still only in its early stages. Named one of America’s Best Startup Employers by Forbes for two consecutive years, Fetch is crafting a people-centric culture rooted in trust and responsibility. How do we achieve this? By empowering personnel to think large-scale, question propositions, and discover novel ways to introduce fun to Fetch. So, why delay? Apply to join our rocketship today!

Fetch is an equal employment opportunity provider.

The ML Engineering team embodies these principles and operates with a laser-like focus to enable intelligent systems for end users, internal stakeholders, and external collaborators. We are seeking a Machine Learning Engineer to partake in this mission and enjoy the benefits of joining an exhilarating enterprise during the high growth phase. Amongst other aspects, Fetch incorporates numerous ML models for every app scan (millions per day and rising), to combat fraudulent activities, and to generate recommendations for users. Machine learning is fundamental to our product, and we’re striving to make it an increasingly integral part of the organization.

Your interest will lie at the crossroads of training ML models and their deployment to production. MLE’s at Fetch handle the complete cycle of machine learning on a team. This encompasses managing/cleansing/piping data, training models for progressive enhancements, and launching those models to production. This will be undertaken in partnership with backend engineers and data scientists in a product team. You’ll be held accountable to add value in a swiftly moving environment, which may at any given point involve delving deep into any one of these stages of the pipeline.

Are you adept at training and deploying a Transformer model but aware of when a simpler solution is appropriate? Do you appreciate understanding how model architectures convert into flops and remove milliseconds from a server? Have you expended entire days troubleshooting impenetrable CUDA errors? If you answered affirmatively to these queries, we’d love to hear from you.

Technical Skills:

Excellent programming abilities (we utilize a lot of Python in this issue area but proficiency in other languages is also welcome)
Experience in training ML models using a Python framework such as Pytorch, TF, etc.
Experience launching a model in a production environment with substantial traffic. We process 100s of events every second in our production pipelines
Experience engaging with a significant-sized codebase (minimum thousand lines) over a prolonged time

Bonus Points For: