Senior Machine Learning Engineer

Senior Machine Learning Engineer About CloudX

At CloudX we’re building a new supply-side advertising platform for mobile publishers. We’re convinced that an AI-native product will significantly advance the state of the art in mobile advertising; we recently raised a $30M Series A in order to bring this dream to life. We have a long history of innovation in this space — our founding team previously built MoPub (sold to Twitter for $350M) and MAX (acquired by App Lovin).

Our new platform combines real technical improvements like verifiably fair auctions with a truly AI-native product experience to give publishers unprecedented control over their ad monetization strategy.

Our Engineering team is distributed and remote— spanning UTC-8 to UTC+6, with core working hours of roughly the US Eastern business day. We have a strong ownership culture and are heavily collaborative, relying primarily on asynchronous, written, communication for coordination. We ship daily and believe that fast CI and good test coverage is the best way to remain productive as we scale.

We’re small and high trust; we optimize for rapid iteration and experimentation. Everyone has access to the latest AI tools, but rather than generating vibe-slop we use them pragmatically to build better products. We are lucky to work closely with our talented Product and Business teams to make sure we’re building the right things. It’s a true early-stage startup with lots of important work to go around.

We are looking for a Senior Machine Learning Engineer to own one of the most important product bets we're making: replacing the narrow set of manual knobs our publishers use to drive revenue — per-line-item floors, bidder targeting, waterfall ordering — with ML-driven systems that optimize life-time customer value automatically. Our long-term vision is for CloudX to be the simplest ad monetization platform;

customers express their basic constraints and desired ad setups, and our machine learning algorithms and agents work together to make suggestions and work within those constraints in order to maximize LTV.

We’ll be our first dedicated ML hire and you will be directly responsible for delivering this vision.

• Machine Learning Engineering
  : design, train, evaluate, and ship the models that power the revenue-optimization product. You'll own the full lifecycle, from feature definition through production deployment and online evaluation. You'll make the architectural calls — what models, what training framework, what serving approach — and you'll write the code to make them real.
• Product Ownership
  : the "make me more money" button is a multi-year product surface, starting with floor pricing, extending into waterfall and bidder-order optimization, and eventually joint optimization across the full set of publisher controls. You'll work directly with Product and with publishers to understand what's actually worth optimizing for and sequence the roadmap accordingly.
• Technical Leadership
  : lead by example to build out the ML discipline ay, several engineers across backend and infra contribute to the ML effort as part of their broader work; you'll be the person setting direction, raising the bar, and — as the function grows — helping us hire and mentor additional ML engineers.

We’re looking for someone who has done this before. The skill we are hiring for is specifically the ability to take an ML system from "theoretically promising" to "demonstrably moving real revenue in production", and we'd like to see concrete evidence of that in your past work. We encourage you to apply if you meet these requirements:

• You’ve shipped ML into a production request path. Not a batch job, not a notebook, not a dashboard. A model…