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Viant

Omnichannel DSP with first-party data

Senior Applied Scientist

Applied Machine LearningConfirmed live in the last 24 hours
Full-time$160k - $200kIn-person

About the Job

Requirements

  • Three to five years of experience developing and applying machine learning models, ideally in production or research environments with measurable outcomes.
  • A 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 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.

Responsibilities

  • 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 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 the research-oriented team through technical communication, code and model reviews, experimentation, and knowledge sharing.

Desired Qualifications

  • Experience with digital advertising, real-time bidding, audience modeling, ad ranking, personalization, or large-scale recommendation systems.
  • Experience with distributed computing, cloud platforms, large language models, generative artificial intelligence, or multimodal artificial intelligence.

About the Company

Viant

Omnichannel DSP with first-party data

Viant is a people-based advertising software company that runs Adelphic, a self-service demand-side platform (DSP) for omnichannel digital advertising. It lets ad buyers plan, create, execute, and measure campaigns across TV, mobile, desktop, audio, and digital out-of-home, including connected TV with IPv6 support. The platform uses Viant’s first-party data linked to about 115 million households and over 1 billion connected devices, combined with more than 280,000 audience attributes from 70+ data partners, to enable scaled, accurate audience targeting and attribution. What sets Viant apart is its emphasis on people-based data for cross-channel targeting, its IPv6-enabled CTV capabilities, and its integrated, self-serve workflow. The company's goal is to help advertisers reach the right people at the right time across all channels, with clear measurement and attribution for their investments.

Company size
201-500
Company stage
IPO
Headquarters
Irvine, CA
Founded
1999

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