Top Tech Jobs & Startup Jobs in Seattle, WA

Lead and scale a Support Engineering team handling complex API and payments issues, own incident response and SLAs, build operational processes, drive metrics and automation, partner cross-functionally, and hire and develop team members.

Lead RevOps analytics and GTM engineering to scale revenue processes. Build analytics infrastructure, evaluate AI-first tech, automate lead-to-cash workflows using AI-assisted development, integrate systems, and deliver data-driven strategic insights across Sales, Marketing, and Post-Sales.

As a Data Scientist at Sardine, you'll design and deploy data-driven solutions to prevent fraud, collaborating with clients and engineering teams to build and optimize machine learning models.

Lead a globally distributed QA team to define QA strategy, governance, and audit-ready processes for regulated banking clients. Drive test automation, CI/release gating, regression coverage, incident response, and partner with Professional Services for UAT and integration validation while reporting quality KPIs.

Lead the development of device intelligence and fingerprinting systems by designing backend services, collaborating on integrations, and applying advanced machine learning algorithms to enhance security and combat fraud.

Sell Sardine's agentic risk platform to enterprise accounts (Fortune 1000, financial institutions, fintechs). Build executive relationships, run discovery, tailor demos, manage complex sales cycles end-to-end, lead negotiations, close deals, and provide market feedback cross-functionally.

Sell Sardine's fraud and risk platform to large banks and Fortune 1000 enterprises. Build executive relationships, run discovery, manage complex multi-threaded sales cycles, coordinate demos and negotiations, and close deals while providing market feedback to cross-functional teams.

Embed with clients to gather requirements and design AI-driven solutions for fraud and fincrime. Architect, train, evaluate, and deploy custom AI agents and workflows, optimize model performance, establish risk metrics and evaluation pipelines, and collaborate with engineering/product teams to scale into production.