Job description

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. Contribute to a rigorous, research-oriented team culture through technical communication, code and model reviews, experimentation, and knowledge sharing. MUST HAVE 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, with 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 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 learning and decisioning. LIFE AT VIANT Investing in our employee’s professional growth is important to us, but so is investing in their well-being. That’s why Viant was voted one of the best places to work and some of our favorite employee benefits include fully paid health insurance , paid parental leave and unlimited PTO and more. Base compensation range: $160,000 - $200,000 In accordance with California law, the range provided is Viant’s reasonable estimate of the compensation for this role. Final title and compensation for the position will be based on several factors including work experience and education. #LI-AM1 About Viant Viant Technology (NASDAQ: DSP) is an exclusively buy-side, AI-powered advertising platform purpose-built for CTV. Viant uniquely combines proprietary content intelligence, household-level identity resolution, and person-level attention signals to connect advertisers with real customers and drive measurable outcomes across the open internet. Through its award-winning AI solutions, Viant is building the future of autonomous advertising, where AI doesn't just assist the campaign, it delivers real results. Learn more at viantinc.com. Viant is an equal opportunity employer and makes employment decisions on the basis of merit. Viant prohibits unlawful discrimination against employees or applicants based on race (including traits historically associated with race, such as hair texture and protective hairstyles), religion, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, reproductive health decision making, gender, gender identity, gender expression, age, military status, veteran status, uniformed service member status, sexual orientation, transgender identity, citizenship status, pregnancy, or any other consideration made unlawful by federal, state, or local laws. Viant also prohibits unlawful discrimination based on the perception that anyone has any of those characteristics, or is associated with a person who has or is perceived as having any of those characteristics. By clicking “Apply for this Job” and providing any information, I accept the Viant California Personnel Privacy Notice.

Requirements

Must-haves

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.
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, with 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 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.
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 learning and decisioning.

Nice-to-haves

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningpythonai

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Health Insurance

Pto

Parental Leave