> Markdown version of [/jobs/ext/3049930-director-applied-science-ad-optimization-ml-systems](https://www.wearedevelopers.com/jobs/ext/3049930-director-applied-science-ad-optimization-ml-systems). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Director, Applied Science - Ad Optimization & ML Systems - **Company:** Viant - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Salary:** $230,000.0 - $260,000.0 - **Contract:** Permanent contract - **Skills:** Machine Learning, Information Technology, Machine Learning Operations - **Published:** September 24, 2026 - **Apply:** https://startup.jobs/director-applied-science-ad-optimization-ml-systems-viant-technology-company-10166091 ## About the Role * 10+ years of experience building and deploying production machine learning systems, with at least 5+ years in a hands-on technical leadership role * Direct experience with real-time, high-scale decisioning systems - ad optimization, bidding, ranking, recommendation, or a closely comparable domain with similar latency and volume constraints * Demonstrated ability to formulate an ambiguous business problem into a precise ML specification: labels, training population, objective, loss function, and evaluation metrics - without relying on framework-level generalities * Working fluency in the operational demands of production ML: delayed feedback, latency and serving tradeoffs, monitoring, drift, and retraining * A track record of technical leadership: reviewing and improving other scientists' system designs, not just managing their output * Bachelor's degree in Computer Science, Engineering, or a related field; Master's preferred GREAT TO HAVE * Direct experience with real-time bidding, ad auctions, or programmatic advertising systems specifically * Experience with identity resolution or cross-device/cross-platform user matching * Experience with agentic or multi-step autonomous decisioning systems * PhD in Machine Learning, Computer Science, or a related field * Publications or conference contributions in ML or a closely related field * Formal people-management tenure beyond the 5-year hands-on leadership requirement above ## Description * Own the end-to-end design, deployment, and performance of Viant's real-time prediction and bid-optimization systems - models that run inside strict latency budgets and directly drive auction outcomes * Define the technical formulation for ambiguous ad-optimization problems: what's being optimized (click, conversion, ROAS, or incremental value), who the valid training population is, what the model should predict, and how a downstream system should interpret it * Provide technical and strategic leadership to a team of applied scientists and ML engineers - reviewing designs, coaching through production tradeoffs, and setting technical direction * Own the operational realities of production ML at scale: delayed feedback and attribution, label imbalance, latency and serving constraints, monitoring, drift, and retraining * Evaluate and improve the systems that decide how bids are placed and adjusted in live auctions, incorporating budget, volume, and business constraints into the model's output * Collaborate with Product and Engineering leadership to align technical roadmap with business priorities * Build toward more autonomous, multi-step decisioning systems as the team's technical roadmap expands beyond single-prediction models ## Related Videos - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)](https://www.wearedevelopers.com/videos/752-how-we-built-a-machine-learning-based-recommendation-system-and-survived-to-tell-the-tale) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Enabling intelligent logistics automation: home-grown Industrial IoT platform at Austrian Post](https://www.wearedevelopers.com/videos/2018-enabling-intelligent-logistics-automation-home-grown-industrial-iot-platform-at-austrian-post) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [What non-automotive Machine Learning projects can learn from automotive Machine Learning projects](https://www.wearedevelopers.com/videos/397-what-non-automotive-machine-learning-projects-can-learn-from-automotive-machine-learning-projects) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [How to start an AI project for a good cause and boost your career](https://www.wearedevelopers.com/magazine/15-how-to-start-an-ai-project-for-a-good-cause-and-boost-your-career) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Prompt Engineering is a Job of the Past](https://www.wearedevelopers.com/magazine/342-prompt-engineering-is-a-job-of-the-past)