Irvine, United States; Los Angeles, United States; United States
Full job description
Viant’s Machine Learning team builds autonomous advertising systems for real-time decisions in targeting, ad optimization, bidding, measurement, and personalization. The Applied Scientist role focuses on applying reinforcement learning and decision-making methods to improve ad selection, ranking, and bidding. Responsibilities include developing and evaluating reinforcement learning and contextual bandit models, studying auction dynamics and reward design, translating research into production models, designing offline and online experiments, partnering with engineers to deploy and monitor models, applying statistical modeling to advertising metrics, and collaborating with cross-functional teams. Requirements include 3-5 years of machine learning experience, strong foundation in ML and statistics, practical Python and PyTorch/TensorFlow experience, and knowledge of reinforcement learning, contextual bandits, and online experimentation. Benefits include fully paid health insurance, paid parental leave, and unlimited PTO. Base salary range is $170,000 - $200,000.
What you'll do
Develop, train, and evaluate reinforcement learning, contextual bandit, ranking, and prediction models for ad optimization, bid optimization, targeting, and personalization
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Irvine, United States; Los Angeles, United States; United States
Full-time
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 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
Contribute to a rigorous, research-oriented team culture through technical communication, code and model reviews, experimentation, and knowledge sharing
Requirements
3-5 years of experience developing and applying machine learning models, ideally in production or research environments with measurable outcomes
Strong foundation in machine learning, deep learning, probability, statistics, and optimization
Practical experience using Python and frameworks such as PyTorch or TensorFlow
Coursework, research, internship, or project experience with reinforcement learning, contextual bandits, sequential decision-making, recommendation systems, online experimentation, or related methods
Ability to formulate a machine learning problem precisely, including objectives, labels, features, loss or reward functions, evaluation metrics, and experimental design
Experience analyzing large-scale data and communicating technical findings clearly to scientists, engineers, and cross-functional partners
Interest in building models that improve real-world decisions in production systems
Experience with reinforcement learning in advertising, marketplaces, recommendation systems, robotics, games, or other sequential decision-making environments
Exposure to contextual bandits, off-policy or counterfactual evaluation, causal inference, auction theory, or online experimentation
