Top Tech Jobs & Startup Jobs in Seattle, WA

Lead Moloco’s global product marketing strategy across its AI advertising portfolio. Own product naming, positioning, messaging, enablement, content, thought leadership, website strategy, launches, and product storytelling. Build the product marketing operating model, integrate marketing into the product lifecycle, partner with product and go-to-market teams, manage segment and geographic marketing, lead a high-performing team, and represent Moloco publicly on AI advertising and industry trends.

As an Applied Scientist II, you will work with cross-functional teams on projects that optimize software systems and improve ML components, while analyzing inefficiencies and proposing solutions.

Lead the development and strategy for Moloco Ads in areas like supply exchange integrations and invalid traffic mitigation, overseeing a large engineering team.

Lead product strategy and P&L for an end-to-end ad product, drive market launches and scaled adoption, identify growth and partnerships, deliver advertiser-centric experiences, and influence cross-functional teams to innovate and grow revenue.

As a Machine Learning Engineer, you'll design and deploy large-scale models for advertising and commerce systems, optimizing for performance and relevance while maintaining ML pipelines.

Lead design and implementation of experimentation infrastructure, including experiment management, configuration delivery, and analysis pipelines. Own technical decisions, improve system reliability and data quality, collaborate with cross-functional teams, and mentor engineers to scale company-wide experimentation.

Lead product strategy for Signals and Identity within Moloco Ads, building privacy-conscious identity graphs, evaluating and combining signals, improving recommendations and targeting, and measuring signal value to drive model and advertiser performance.

Design, develop, and optimize large-scale distributed data systems for real-time and batch processing. Support feature engineering and feature store evolution, build identity graph capabilities, improve observability and performance, and mentor engineers while collaborating with Product, ML, and Data Science teams.