vianttechnology · Irvine, California, United States; Los Angeles, California, United States; United States
Skills: Python
WHAT YOU’LL DO Viant’s Machine Learning team is building autonomous advertising systems that make real-time decisions across targeting, ad optimization, bidding, measurement, and personalization. These systems process hundreds of millions of events daily and operate in the high-throughput, low-latency environment of programmatic advertising. As an Applied Scientist, you will apply reinforcement learning and related decision-making methods to improve how Viant selects, ranks, and bids on advertising opportunities. You will work across contextual bandits, exploration and exploitation, counterfactual learning, and model-based experimentation to turn research into production systems that improve campaign performance, auction efficiency, and measurable business outcomes. THE DAY-TO-DAY Develop, train, and evaluate reinforcement learning, contextual bandit, ranking, and prediction models for ad optimization, bid optimization, targeting, and personalization. Study auction dynamics, delayed feedback, exploration and exploitation, budget constraints, pacing, and reward design to improve real-time advertising decisions. Translate research ideas into production-ready models that operate reliably at high throughput and low latency across Viant’s advertising platform. Design and analyze offline and online experiments, including counterfactual and off-policy evaluation where appropriate, to measure model quality and incremental business impact. Partner with engineers to deploy, monitor, retrain, and improve models in production, addressing issues such as data leakage, class imbalance, drift, calibration, and changing market conditions. Apply quantitative reasoning and statistical modeling to problems involving click-through rate, conversion, return on ad spend, targeting, attribution, identity, and measurement. Collaborate with scientists, engineers, and product partners to define objectives, labels, loss functions, reward signals, evaluation metrics, and practical delivery plans…
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