Staff Applied Scientist
Viant Technology Inc - Irvine, California, United States; Los Angeles, California, United States
Posted Oct 6, 2025
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Salary
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- 401(k) match
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Schedule
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Staff Applied Scientist Irvine, California, United States; Los Angeles, California, United States WHAT YOU'LL DO Viant's Machine Learning team is at the forefront of transforming the Ad Tech industry through cutting-edge machine learning and AI-driven automation. By eliminating manual processes in digital ad creation, optimization, and measurement, we build autonomous, scalable systems that process hundreds of millions of events daily. We are seeking an exceptional Staff Applied Scientist to drive groundbreaking innovation in applied machine learning. In this role, you will lead end-to-end AI strategy, shape the applied science roadmap, and develop next-generation models in NLP, deep learning, and large-scale AI systems. Your expertise will push the boundaries of AI capabilities, delivering measurable business impact while fostering a research-driven, data-centric culture at Viant. THE DAY-TO-DAY - Lead and Define AI Strategy - Set the vision for applied ML research and AI-driven decision-making across Viant's products and services. - Drive High-Impact Research - Conduct advanced research in NLP, RL, LLMs, and deep learning, publishing in top-tier conferences while applying findings to real-world production systems. - Architect and Deploy ML at Scale - Develop, optimize, and deploy high-throughput, low-latency ML models, collaborating with engineers to bring solutions into production. - Innovate in Generative AI & Causal ML - Explore novel AI techniques, such as LLMs, reinforcement learning, and causal inference, to enhance targeting, personalization, and attribution models. - Experimentation and Validation - Design, run, and analyze A/B experiments to test ML-driven strategies, ensuring data-driven decision-making. - Mentor and Influence - Provide technical
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