Machine Learning Engineer - Core Signals

Yobi is a rapidly growing Behavioral AI company on a mission to ethically democratize the benefits of data and AI.

Since 2019, we have built one of the largest consented behavioral datasets in the United States, extending far beyond the walled gardens of Big Tech. Unlike traditional LLM companies, Yobi builds foundation models of human behavior grounded in real-world actions such as purchases and store visits.

Our private-by-design modeling enables state-of-the-art personalization and decisioning for leading brands and agencies while protecting privacy, safety, and ethics.

Today, we are focused on bringing the performance of closed-web user acquisition to the open web and connected TV, giving brands walled-garden results without the walls.

At our core, Yobi is building the behavioral intelligence layer for any system that makes a personalization decision.

Working at Yobi

We’re at an inflection point—customer adoption is accelerating, but there’s still room to shape the architecture and culture from the ground up. Engineers here own major surface areas, build 0→1 systems in large-scale data and model infrastructure, and help define how Behavioral AI scales ethically and effectively.

Highlights:

• Well-funded with 5+ years of runway. At the same time, we are scaling revenue quickly and project to be breakeven in 2026.

• Partnerships with Microsoft and Databricks

• Fully remote or hybrid from several hubs (SF Bay Area, Seattle, NYC)

• World-class team of Machine Learning experts who worked on cutting edge infra and recommender systems @ Amazon, Uber, Twitter, Meta, etc.

• Product and Go-To-Market teams who have taken ideas from concept to 9 figure revenue streams

Benefits:

• Competitive Base Salary

• Meaningful equity & financial upside - a real % of the company

• Annual bonus target based on personal and company performance

• Health, Dental, Vision - most plans will pay little to 0 out of pocket

• Unlimited PTO - we care about impact, not tracking days you’re out

• 401k with company match %

As an MLE on the Core Signals team, you will primarily be focused on (1) the representation learning surrounding our fundamental user-behavioral modeling problems and (2) using those core models to power new and existing products.

This role involves a lot of collaboration with the Product org and Applications team to realize these R&D gains - but is ultimately a "full stack" ML role. Day to day responsibilities include data processing, model training, deployment, and evaluation.

A good amount of "wearing your Product hat" is expected, as well as the ability to flex into some other functions as needed - we are a quickly growing startup after all.

• You understand enough about machine learning to be able to apply it to novel problems and suggest improvements to our current setup, or even radical new approaches. Previous publication experience is not required.

• You've worked on and can speak to at least some of: representation learning, embeddings drift, CTR modeling, sequence modeling, etc., preferably on "big data" in an industrial setting.

• Skill and attitude wise, you can quickly contribute to the non-research components of our pipeline. This includes things such as data orchestration, build systems, and experiment tracking. Although we use a combination of open source products like Airflow, Bazel, Github CI/CD, and Spark, prior experience with these specific solutions is not needed. However, a good part of your day to day will involve interacting with these systems, so you should be comfortable with getting your hands dirty.

• Good product sense - you have 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.