About Moloco

Moloco is an AI-native performance advertising company, bringing the power of AI advertising to the open internet. Founded in 2013 by machine learning engineers who had built AI advertising systems inside the world's largest technology companies, Moloco began from a shared mission: to build that same engine for the open internet.

Today, Moloco is one company with two businesses, built on the same compound AI system. Moloco Ads is our AI-native performance advertising platform, helping mobile app marketers acquire, retain, and re-engage high-value users across the open internet. Moloco Commerce Media is our AI-native retail media platform, enabling retailers and marketplaces to turn their platforms into thriving ad businesses. Both are powered by CARA (Compound Ad Recommendation Architecture), the AI system behind every Moloco product.

At Moloco, we bring together those who've already solved hard problems and those with the drive to. Here, we work on some of the complex problems in AI advertising, at a scale and level of sophistication very few companies can offer. Come build what's next.

About the Team

The Ads Platform, Experiment team owns Moloco's company-wide experimentation infrastructure. We build the systems that help teams roll out changes safely, measure impact rigorously, and make better product and business decisions with confidence.

Our scope spans experiment lifecycle management, dynamic configuration and feature flag delivery, and the data pipelines and statistical tooling that power experiment analysis. We work at the intersection of backend infrastructure, data systems, and internal product development, partnering closely with engineers, data scientists, and statisticians.

About the Role

As a Senior Software Engineer on the Experiment Platform team, you will lead the design and evolution of high-leverage systems that shape how Moloco runs experiments across products. You will own important technical areas end-to-end, drive sound engineering decisions in ambiguous problem spaces, and build platforms that improve the speed, safety, and trustworthiness of experimentation company-wide.

This role is well-suited for engineers who combine strong execution with technical judgment, enjoy working across backend and data system boundaries, and want to raise the quality bar for an internal platform used by many teams.

Responsibilities

Lead the design and implementation of major components across experiment management services, configuration delivery systems, and analysis pipelines.
Own technical decisions for platform areas that require balancing correctness, reliability, performance, and ease of adoption.
Improve the trustworthiness of experimentation by strengthening system design, observability, data quality, and operational standards.
Partner closely with engineers, data scientists, statisticians, and stakeholders to turn ambiguous requirements into scalable platform capabilities.
Mentor other engineers through design discussions, code reviews, and pragmatic technical leadership.

Minimum Qualifications

5+ years of professional software engineering experience in a production environment.
Strong hands-on experience in backend systems, data infrastructure, distributed systems, or other relevant platform areas.
Proven ability to independently drive technical projects from problem definition through production rollout in ambiguous problem spaces.
Strong engineering judgment in system design, operational reliability, maintainability, and cross-functional execution.
Bachelor's degree in Computer Science, Computer Engineering, or equivalent practical experience.

Preferred Qualifications

Experience designing and implementing experimentation platforms, A/B testing systems, feature flagging systems, or dynamic configuration platforms.
Experience owning platform or infrastructure systems used by multiple engineering teams.
Experience working with analytical data systems, metrics pipelines, or data quality and correctness challenges.
Familiarity with advertising, mobile product ecosystems, or performance-driven product environments.