Senior Product Designer, Monetization

Microsoft•Redmond, WA

19h•$160,200 - $261,000•Onsite

About The Position

The Senior Product Designer will join the Monetization Inventory Design team and shape current and future monetization experiences across Microsoft Copilot, Bing (including AI Overviews and AI Mode), MSN, gaming, and other consumer surfaces. The work spans existing advertising experiences and new AI-enabled models that redefine how people interact with technology and how those experiences create value for consumers and businesses. This role calls for a designer, builder, and systems thinker who reimagines the design workflow for the AI era. Rather than manually creating every artifact, the designer builds tools and orchestrates AI agents and learning systems to generate, critique, evaluate, and improve design work. The role emphasizes strong interaction and visual judgment, customer empathy, product strategy, technical prototyping, and the ability to encode design standards into scalable systems. The designer partners with multidisciplinary teams to turn customer needs and emerging technologies into understandable, useful experiences. The position is based in Mountain View, California, or Redmond, Washington. Applicants for Mountain View must live in the San Francisco Bay Area; Redmond applicants must live in the greater Seattle metropolitan area. The role requires working in-office four days per week. Microsoft AI employees within the stated commute distances of designated offices are expected to work from the office at least four days per week beginning January 26, 2026, subject to local law and jurisdiction.

Requirements

Master's degree in Industrial Design, Product Design, Human Computer Interaction, User Experience, Interaction Design, or a related field and 4+ years of product or service design experience; or bachelor's degree in one of these fields and 5+ years of such experience; or equivalent experience.
Demonstrated experience working in product or service design or using design thinking to solve problems may qualify as equivalent experience.
Strong interaction and visual design judgment, customer empathy, product intuition, and ability to recognize and define quality.
Ability to build and orchestrate AI-powered workflows or agentic systems, including agents that use tools, context, delegation, evaluation, and feedback to accomplish complex work.
Hands-on experience personally building working AI-driven systems, tools, or experiences that materially changed how a problem was explored, designed, or solved; conceptual familiarity with AI alone is insufficient.
Solid understanding of LLMs and generative AI, including prompting, context engineering, model and tool orchestration, structured outputs, retrieval, and evaluation.
Working knowledge of HTML, CSS, JavaScript/TypeScript, Python, or similar technologies, with the ability to independently create and modify functional prototypes and tools.
Experience working with APIs, structured data, models, tools, and external services to construct end-to-end AI experiences.
Solid understanding of evaluation for probabilistic systems, including defining quality criteria, identifying failure modes, and designing feedback and recovery mechanisms.
Ability to establish direction in ambiguous technical spaces and transform emerging technology into useful and differentiated customer experiences.
Solid product judgment and ability to connect customer needs, technology, research, business context, and measurable outcomes.
Ability to explain complex AI systems and build alignment across Product, Engineering, Research, Data Science, Design, and business leadership.

Nice To Haves

Master's degree in Industrial Design, Product Design, Human Computer Interaction, User Experience, Interaction Design, or a related field and 6+ years of product or service design experience; or bachelor's degree in one of these fields and 8+ years of such experience; or equivalent experience.
A portfolio or body of work demonstrating exceptional product, interaction, and visual design judgment, along with experience shipping high-quality digital products.
Experience building multi-agent, recursive, or self-improving systems that evaluate outputs, incorporate feedback, and iteratively improve subsequent work.
Experience creating AI-powered design tooling, automation, or systems that expand the capabilities of other designers.
Exceptional interaction and visual design craft and ability to encode taste and quality into principles, constraints, prompts, evaluations, tools, and systems that people or agents can apply.

Responsibilities

Own and ship high-quality AI-powered consumer experiences from exploration through implementation, maintaining standards for interaction quality, visual craft, accessibility, trust, and customer impact.
Create AI-powered workflows that generate, critique, evaluate, refine, and operationalize design work at scale.
Build and orchestrate teams of AI agents, defining specialized roles, tools, context, memory, responsibilities, delegation, handoffs, evaluation, feedback, and escalation to people.
Develop recursive and learning design systems that use research, experimentation, product signals, evaluation, and human feedback to improve their outputs.
Build custom tools, prototypes, automations, and agentic workflows using code, models, APIs, structured data, and emerging AI technologies.
Encode design principles, customer knowledge, interaction patterns, brand standards, and quality criteria into reusable systems.
Design intelligent product behaviors that provide appropriate understanding and control, including intent, delegation, system status, uncertainty, correction, recovery, and human oversight.
Define quality standards for AI-driven experiences and build evaluation mechanisms to assess quality, identify failure modes, and improve systems.
Partner with Product, Engineering, Data Science, Research, Design, and business teams to connect customer needs with technical capabilities and influence system requirements.
Identify repeated human effort that can be scaled through automation, delegation, orchestration, or continuous learning.
Share tools, agents, patterns, and learnings so individual experimentation becomes reusable organizational capability.