Senior Machine Learning Engineer, Ads Experimentation & Measurements

Unity Technologies•New York, NY

9d•$148,700 - $229,900

About The Position

Unity’s Ads Experimentation Platform team is looking for a senior machine learning engineer to lead the evolution of how we validate and optimize our global advertising ecosystem. At Unity, our ads reach billions of devices, and the ability to rapidly iterate on machine learning models or ads delivery pipelines is critical to our growth. In this role, you will be the technical authority for our experimentation and evaluation roadmap. You will bridge the gap between advanced statistical methodology and large-scale engineering, building the tools that allow ML teams to iterate faster and with higher confidence. You will join a team focused on high-velocity innovation, moving beyond standard A/B testing into the next generation of causal inference and high-sensitivity evaluation methodologies.

Requirements

5+ years of experience in Data Science or Applied Research, specifically within Ad Tech, Marketplaces, or high-scale experimentation platforms.
An MS or PhD in a quantitative field (Statistics, Economics, Computer Science, Operations Research, or equivalent).
Deep expertise in causal inference, experimental design, and frequentist/Bayesian statistics, with a proven track record of applying these to high-volume, real-time data.
Strong programming skills in Python or Scala, and experience with large-scale data processing frameworks like Spark, Snowflake, or BigQuery.
Practical experience implementing advanced testing methodologies like CUPED, interleaving, or switchback testing in production environments.
Ability to translate complex statistical concepts into clear product roadmaps and mentor engineering teams on experimental rigor.
Sufficient knowledge of English to have professional verbal and written exchanges in this language since the performance of the duties related to this position requires frequent and regular communication with colleagues and partners located worldwide and whose common language is English.

Nice To Haves

Experience building or maintaining internal experimentation platforms at a major tech company.
Familiarity with the unique challenges of long-term value (LTV) prediction and surrogate metric design in mobile gaming or digital advertising.
Experience embracing AI as a strategic advantage in engineering, following established best practices for code quality and security

Responsibilities

Hands-on experience evaluating pacing and ad selection systems, with strong domain knowledge of the ads ecosystem (advertiser objectives, bidding/pricing dynamics, attribution, and marketplace mechanics) and practical familiarity with simulation-based measurement to pre-validate model and policy changes before live deployment.
Demonstrated ability to drive product decisions through analytics — defining metrics, building measurement frameworks, analyzing A/B test results, and translating experiment outcomes into clear, actionable insights for ML, Product, and Business partners.
Design the statistical methodologies of our experimentation platform in a complex, budget-constrained environment. You will lead the implementation of variance reduction (CUPED), sequential testing, and interleaving frameworks to maximize sensitivity and accelerate evaluation cycles. You will also build the statistical foundations for automated pipelines that autonomously test and select optimal features and hyperparameters at scale.
Research and validate surrogate metrics that correlate highly with long-term user retention, churn, and value. You will provide ML teams with short-term signals that accurately predict long-term impact, enabling faster optimization for long-term growth.
Build the statistical foundations for automated pipelines that autonomously test and select optimal features and hyperparameters. This reduces manual engineering overhead and accelerates the deployment of high-performing models at scale.
Serve as the lead subject matter expert on experimentation for ML, Product, and Engineering teams. You will ensure statistical rigor is integrated throughout the product lifecycle, from initial model training to live production auctions.