Sr. Applied Scientist
About this role
This Applied Scientist role addresses the challenge of improving real-time decision-making within autonomous advertising systems. Viant Technology is developing sophisticated machine learning systems that manage complex tasks like targeting, ad optimization, bidding, measurement, and personalization for programmatic advertising. The core objective of this position is to leverage advanced decision-making methods to enhance how advertising opportunities are selected, ranked, and bid upon, ultimately driving better campaign performance and measurable business results.
The Applied Scientist will be responsible for translating theoretical research into robust, production-ready models operating in a high-throughput, low-latency environment. This involves applying reinforcement learning and related techniques to areas such as contextual bandits, exploration/exploitation trade-offs, and model-based experimentation. The individual will work closely with engineering teams to deploy, monitor, and iterate on these models, ensuring they effectively address real-world challenges like data drift and model reliability.
This role is ideal for a candidate who enjoys bridging the gap between deep learning research and practical, high-stakes production systems. A successful candidate possesses strong quantitative reasoning skills and a desire to build models that move beyond simple offline accuracy to deliver tangible improvements in live advertising auctions and business outcomes.
What you will do
- 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, 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.
What you need
- Candidates must have 1 to 3 years of experience developing and applying machine learning models, preferably in production or research settings with measurable outcomes.
- A strong foundation in machine learning, deep learning, probability, statistics, and optimization is required, along with practical experience using Python and frameworks like PyTorch or TensorFlow.
- Experience or coursework in reinforcement learning, contextual bandits, sequential decision-making, recommendation systems, or online experimentation methods is necessary.
- The ability to precisely formulate a machine learning problem, including objectives, labels, loss or reward functions, evaluation metrics, and experimental design, is essential.
- Experience analyzing large-scale data and clearly communicating technical findings to scientists, engineers, and cross-functional partners is required.
- A strong interest in building models that improve real-world decisions in production systems, rather than focusing solely on offline accuracy.
- Prior experience with reinforcement learning in advertising, marketplaces, recommendation systems, or sequential decision-making environments is a significant advantage.
Nice to have
- Experience with digital advertising
- Experience with real-time bidding
- Experience with audience modeling
- Experience with ad ranking
- Experience with personalization
- Experience with large-scale recommendation systems
Benefits
- Fully paid health insurance
- Paid parental leave
- Unlimited PTO
Skills and tech stack
Questions candidates ask
What is the typical compensation range for this role?
The base compensation range for this role is $130,000 to $170,000. The final title and compensation will be based on several factors including work experience and education.
What is the primary focus of the role regarding model development?
The primary focus is applying reinforcement learning and related decision-making methods to improve how Viant selects, ranks, and bids on advertising opportunities. The work involves developing models for ad optimization, bid optimization, targeting, and personalization.
What is the expected environment for model deployment?
Models are expected to operate reliably at high throughput and low latency across Viant’s advertising platform. This requires translating research into production-ready models that handle real-time decisioning.
What is the team culture like?
The role contributes to a rigorous, research-oriented team culture through technical communication, code and model reviews, experimentation, and knowledge sharing.
The application is on the official Viant Technology careers page.
Apply on company site