Top Tech Jobs & Startup Jobs in Seattle, WA

Own product areas within AI Stockbroking and other financial services. Define user problems, product scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial products; analyze data and feedback; launch features; monitor performance; and drive continuous iteration while balancing user experience, business impact, compliance, and operational complexity.

Own product areas for KIRA’s AI Stockbroking and other financial services. Define user problems, product scope, success metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify complex financial products. Use research, feedback, metrics, experiments, and production insights to launch features, monitor performance, and drive continuous improvement while balancing speed, experience, compliance, and operational complexity.

Own product areas within an AI-focused neobank across financial services such as insurance, payments, savings, investing, and travel. Define product direction, scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial workflows; and use research, feedback, experiments, and performance data to drive continuous improvement.

Own product areas across AI Finance, including insurance, payments, savings, investing, and travel. Define user problems, product scope, metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify financial products, launch features, monitor performance, and iterate using data, feedback, research, and production insights. Balance speed, user experience, business impact, compliance, and operational complexity.

Own product areas within KIRA’s AI Neobank across financial services such as insurance, payments, savings, investing, and travel. Define user problems, product scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial workflows; and use research, feedback, experiments, and performance data to iterate after launch.

Own product areas within AI Investing and other financial services, defining user problems, product scope, success metrics, roadmaps, and launch plans. Partner with engineering, design, data, operations, compliance, and business teams to simplify complex financial products, launch features, monitor performance, and iterate using data, feedback, research, and production insights. The role requires strong product judgment, execution, prioritization, communication, and ownership in ambiguous environments.

Own AI Finance product areas across insurance, payments, savings, investing, travel, and related financial services. Define user problems, product scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams. Simplify complex financial products, use research and performance data to guide decisions, launch features, monitor results, and drive continuous iteration while balancing speed, customer experience, business impact, compliance, and operational complexity.

Own product areas within AI Investing and related financial services, defining user problems, product direction, roadmaps, metrics, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify complex financial products. Launch features, monitor performance, use data and feedback to drive iterations, and balance speed, user experience, business impact, compliance, and operational complexity.

Own AI Finance product areas across insurance, payments, savings, investing, travel, and other financial services. Define user problems, scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial journeys; and use research, feedback, experiments, and performance data to iterate rapidly after launch.

Owns the end-to-end product strategy and execution for AI-powered systems. Defines requirements, evaluation frameworks, quality standards, and system behavior while balancing quality, latency, cost, reliability, safety, and user experience. Partners closely with ML, backend, and client engineering teams to identify failure modes, improve model performance, prioritize roadmaps, and deliver predictable, useful, trusted AI experiences.