Applied Scientist

WHAT YOU’LL DOViant’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.

Contribute to a rigorous, research-oriented team culture through technical communication, code and model reviews, experimentation, and knowledge sharing.

MUST HAVE1–3 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, with practical experience using Python and frameworks such as PyTorch or Tensor Flow.

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 move beyond offline accuracy and 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.

GREAT TO HAVE Experience with digital advertising, real-time bidding, audience modeling, ad ranking, personalization, or large-scale recommendation systems is a plus.

Experience with distributed computing, cloud platforms, LLMs, generative AI, or multimodal AI is also welcome, but the core focus of this role is production-oriented reinforcement…