Principal Product Manager

Microsoft•Redmond, WA

3d•$142,800 - $304,200•Hybrid

About The Position

The Microsoft 365 Core Copilot Infrastructure team is hiring a Principal Product Manager for its Capacity Management team. The team plans, delivers, and scales the GPU and CPU fleet powering Copilot across Microsoft 365 and helps direct significant annual capacity investment. This broad platform role focuses on taking on high-impact problems across demand forecasting, capacity investment planning, platform and hardware strategy, supply and demand balancing, capacity intake and delivery, and model onboarding and migration. The role helps determine where to invest, when to build, what hardware and models to run, and how to keep the platform healthy and efficient as AI workloads grow. It is an AI-first product management role, using agents, automation, AI-driven analysis, and prototyping alongside standard product management tools to evaluate telemetry, model tradeoffs, and accelerate decisions. The Product Manager translates complex technical and financial tradeoffs into actionable recommendations for leaders and advances AI-forward working practices, including evaluation sets and agent-based workflows. The position partners with engineering leaders, architects, data scientists, finance, supply chain, and Copilot scenario teams across Microsoft. It requires product judgment, technical depth, data fluency, customer empathy, and the ability to exercise clear judgment under uncertainty. As a senior team member, the Product Manager mentors other product managers and helps improve the team's practices. The role is based in Redmond, Washington, and requires working from the office at least three days per week.

Requirements

Bachelor's degree and 8+ years of experience in product, service, or program management, or software development, or equivalent experience.
Proven ability to think at the platform level while maintaining a strong customer mindset and translating customer and partner needs into durable platform capabilities.
Experience taking ambiguous, high-stakes work from strategy through execution across organizations.
Experience leading large-scale cloud infrastructure, capacity planning, or distributed systems work, with an understanding of hyperscale service delivery.
Experience with LLMs, model-serving platforms, GPU capacity planning, or large-scale AI-powered products.
AI fluency, including using AI agents, automation, and prototyping to accelerate analysis, decisions, and product work.
Experience using telemetry, demand signals, and operational data to guide platform and investment decisions.
Experience mentoring and developing product managers and technical leaders.
Systems-level problem-solving and clear communication skills, including the ability to influence senior leaders and align stakeholders across organizations.

Nice To Haves

Bachelor's degree and 12+ years of experience in product, service, or program management, or software development, or equivalent experience.
4+ years of experience taking a product, feature, or experience to market, such as through design, product-market fit, and launch, including internal tools or frameworks.
6+ years of experience improving product metrics for a product, feature, or experience in a market, such as growing the customer base, expanding usage, or avoiding customer churn.
6+ years of experience disrupting a market for a product, feature, or experience, such as through competitive disruption or replacing an established competing product.

Responsibilities

Shape strategy for capacity and platform work across M365 Copilot and turn it into an executable roadmap.
Drive high-impact problems across capacity intake and planning, supply and demand balancing, buildout to SLA, GPU utilization and platform health, and model onboarding and migration.
Partner with Copilot scenario teams to understand customer needs and translate them into platform capabilities and capacity investments.
Use AI-driven analysis of demand signals, telemetry, and operational data to improve forecasting, identify capacity and reliability issues early, and address root causes.
Use agents, automation, and AI Ops to analyze data, coordinate work, and turn manual, ticket-driven intake and allocation into intelligent self-service systems.
Model capacity and hardware tradeoffs through AI prototyping and scenario testing before investment commitments.
Balance cost, performance, and reliability and prioritize multi-year capacity investments with engineering, finance, and supply chain partners.
Advance platform, model, and hardware transition strategy and improve platform efficiency as AI workloads grow.
Set success metrics, OKRs, and KPIs, and shape investment priorities with leadership.
Explain complex technical and financial tradeoffs to executives to support decision-making.
Mentor and develop other product managers and advance AI-forward ways of working.