Top Tech Jobs & Startup Jobs in Seattle, WA

Lead and grow a Data Engineering team to build production data pipelines, medallion datasets, and reusable data products. Drive architecture, engineering standards, design reviews, and cross-functional delivery. Partner with Product, Analytics, Data Science, and Platform to enable governed, reliable data foundations for analytics and AI. Mentor engineers and manage multiple concurrent initiatives to deliver predictable outcomes.

Lead strategy and execution for Scribd's Search product, defining multi-year roadmaps and metrics, partnering with ML, engineering, design, and analytics to ship ML-driven retrieval and ranking features that improve relevance, engagement, and conversion across large-scale consumer surfaces.

Lead strategy, vision, and roadmap for a cross-functional squad across Everand and Fable. Drive product development for recommendations, search, and ranking systems, balance experiments and large investments, partner with engineering, design, research, and data to ship features that help readers discover books.

Own product and lifecycle growth marketing for Everand and Fable: define positioning and GTM, build and ship campaigns across email/push/in-app in Iterable, set metrics and tracking, iterate on messaging and targeting, and partner with Product and Brand to drive adoption and growth.

Lead development of the Fable Android app: design and build high-quality experiences, drive architecture and performance improvements, collaborate with product/design/QE, mentor engineers, improve code quality and release reliability, and own features from concept to release.

Own and set technical direction for the Everand backend, building scalable, reliable systems for discoverability, personalization, and core consumption journeys. Drive architecture, platform health (CI/CD, staging, DBs, queues, cost, observability), mentor engineers, lead cross-team planning, modernize legacy systems, and partner with product, design, analytics, web/mobile, and ML to support experimentation and telemetry-driven decisions.

Design, develop, and deploy machine learning and NLP solutions at scale. Work with cross-functional teams to build content classification, representation learning, search, and recommendation models using classical ML, deep learning, and LLMs. Process large datasets with Python, SQL, and Spark, and communicate results to stakeholders with clear documentation.