Yobi is a rapidly growing tech company in the behavioral modeling and personalization space. Our mission is to Ethically Democratize the AI Revolution. Since our founding in 2019, we have been busy assembling the largest consented user-behavioral dataset in the world outside the walled gardens of BigTech. We use that dataset to help companies, big and small, supercharge the work of their Machine Learning and Marketing teams in privacy protective ways.
At Yobi we believe that every employee should be empowered to own 0-1 contributions and have the opportunity to achieve real impact - and scale that impact - on the business. If that sounds like an exciting opportunity to you, we want to hear from you.
Yobi Highlights:Partnerships with Microsoft and DatabricksFull remote or hybrid from several hubs (Boston, SF Bay Area, Seattle, NYC)Team of ML Experts who worked on cutting edge recommender systems and internal tools @ Amazon, Uber, Twitter, Meta, etc.Experts on the Product and Go-To-Market fronts who have taken ideas from concept to 9 figure revenue streamsBenefits:Competitive Base SalaryMeaningful equity & financial upside - a real % of the companyHealth, Dental, VisionFlexible PTO401kAbout The RoleAs a MLE on the Data Monetization team, you will primarily be focused on building the best systems to ingest partner data in a privacy-safe way, and getting the most out of the data to power the rest of our models and products.
This role involves a lot of collaboration with the Product org and working with customers directly in order to build private-by-design technology (embedding models where one party controls the code and the other controls the data, provably encrypted ways to share identifiers, etc.).
Significant "wearing your Product hat" is expected, along with driving results in the many domains required to deliver whole ML-powered products - we are a quickly growing startup after all!
What it takes to succeed in this role:Can think creatively about data - are there ways to extract signal from partially obfuscated sources?Knows when to use a Data Clean Room and when it's just a buzzwordUnderstanding enough about machine learning to be dangerous but not necessarily published in fieldWorked on and can speak to some kinds of impactful consumer-facing ML problems, e.g. recommender systems, personalization, etc.Skill and attitude wise, can quickly contribute to things such as orchestration/Airflow, Bazel (build systems, really), CI/CD, Spark (we have both Python and Scala), and other SQL-y data computation backends as needed. Not everybody is going to be a rockstar on all of these, and we are making novel tech investments across the company, but successful engineers will see opportunity and a path to (first) PR, instead of "not my job".Good product sense, has opinions on what we should and shouldn't be doing both in chasing product-market fit and on the implementation side.We prioritize attitude, culture, and general (technical) fit over matching perfectly into one of our job descriptions. If our mission and work resonates with you, we encourage you to apply. Tell us how you can help drive our products forward, even if you don't feel like you are a perfect fit for some of the listings.
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