(USA) Senior Director, Product Management

Walmart•Bentonville, AR

7d•$160,000 - $416,000

About The Position

Walmart is building an Enterprise AI Platform to provide a common foundation for AI workloads across the company. The platform spans MLOps, model and inference access, LLM gateway services, developer experience, model lifecycle management, observability, governance, reliability, and AI economics. It is intended to help teams build and operate AI solutions quickly, safely, reliably, and economically at enterprise scale. The Senior Director of Product Management will define the platform’s product vision, strategy, and execution. The role calls for understanding the direction of AI technology, identifying durable enterprise platform capabilities, and translating rapid change into coherent product strategy. The leader will work with engineering leaders, architects, AI and infrastructure teams, governance and security partners, and business leaders on decisions involving models, infrastructure, developer experience, safety, economics, and platform architecture. The role combines technical product leadership, strategic thinking, executive communication, financial discipline, and action-oriented execution. The successful candidate will bring clarity to ambiguity, stay grounded in customer and engineering realities, and build capabilities that operate reliably at global scale. The position is based in Bentonville, Arkansas or Sunnyvale, California. The annual salary range is $160,000–$320,000 in Bentonville and $208,000–$416,000 in Sunnyvale. Additional compensation may include annual or quarterly performance bonuses and, for certain positions, stock.

Requirements

Extensive product leadership experience building and scaling AI/ML platforms, developer platforms, cloud platforms, infrastructure products, or other highly technical enterprise platforms.
Demonstrated success defining platform strategy and translating it into roadmaps, technical capabilities, operating mechanisms, adoption, and measurable outcomes.
Strong understanding of machine learning, generative AI, foundation models, multimodal systems, AI agents, inference, MLOps, model lifecycle management, evaluation, observability, and AI infrastructure.
Sufficient technical depth to engage credibly with senior engineers, architects, distinguished technical leaders, infrastructure teams, and AI experts on complex architectural and product decisions.
Strong product judgment and ability to identify reusable platform capabilities that create enterprise leverage rather than simply address individual feature requests.
Demonstrated understanding of APIs, platform abstractions, developer workflows, distributed systems, reliability, performance, and cloud infrastructure.
Strong understanding of AI economics, including model pricing, token consumption, inference costs, infrastructure utilization, capacity, and cost-performance tradeoffs.
Ability to establish first principles, create structure from incomplete information, and drive decisions and execution in rapidly evolving and ambiguous environments.
Strong strategic and systems thinking, connecting customer needs, technology evolution, architecture, economics, risk, and enterprise strategy.
Exceptional storytelling and executive communication skills, including the ability to explain sophisticated technical concepts clearly while preserving the substance needed for sound decisions.
Strong listening skills, maturity, self-awareness, and intellectual flexibility; ability to form clear points of view, challenge assumptions, and change direction when evidence warrants it.
Demonstrated ability to influence senior leaders and highly technical teams across organizational boundaries through credibility, clarity, and results.
Strong bias for action and practical problem solving, balanced with judgment about when foundational decisions require deeper consideration.
Strong business and financial acumen, including the ability to connect platform investments and technology decisions to cost, efficiency, adoption, risk, and business value.
Experience building and developing strong product teams and senior product leaders.
Entrepreneurial mindset combined with enterprise leadership maturity and the ability to build capabilities that operate reliably at global scale.
Minimum qualification: Bachelor’s degree in computer science, engineering, or a related area and 13 years of product management experience; or 15 years of experience in product management or a related area.

Nice To Haves

Experience leading product strategy for AI/ML platforms, MLOps platforms, inference or model gateways, GenAI platforms, developer platforms, or large-scale cloud infrastructure.
Experience across commercial and open model ecosystems and understanding of the technical and economic implications of model optionality.
Experience with model gateways, routing, quotas, capacity management, inference optimization, evaluation, prompt or model lifecycle management, agents, and runtime observability.
Experience building platforms that support traditional ML as well as generative or agentic AI workloads.
Experience with AI governance, Responsible AI, security, privacy, or safety engineering.
Experience owning products with significant infrastructure or third-party consumption costs and improving unit economics.
Experience making build-versus-buy decisions and working with cloud providers, model providers, open-source technologies, and emerging AI companies.
Experience leading global product teams and influencing highly distributed engineering and business organizations.
Master’s degree in Computer Science, Engineering, Business Administration, or a related area, and 12 years of product manager experience, ideally on consumer-facing, large-scale, highly complex B2B/C products.
Supervisory experience.
Background in creating inclusive digital experiences, including knowledge of WCAG 2.2 AA, assistive technologies, and digital accessibility best practices.

Responsibilities

Define the long-term vision and strategy for Walmart’s Enterprise AI Platform and translate Walmart’s AI ambitions into product priorities, platform capabilities, and investment decisions.
Anticipate changes in foundation models, multimodal AI, agents, inference technologies, orchestration, and AI infrastructure, and evolve the platform ahead of enterprise demand.
Identify durable shared platform capabilities across models, providers, infrastructure, lifecycle management, governance, and developer workflows.
Enable teams to choose models and infrastructure based on capability, quality, performance, cost, availability, and risk while minimizing unnecessary lock-in.
Improve how engineers, data scientists, and product teams discover, onboard, experiment with, evaluate, deploy, observe, and operate AI workloads.
Develop platform capabilities and mechanisms that help teams understand and optimize model consumption, infrastructure utilization, unit economics, capacity, and cost-performance tradeoffs.
Partner with AI Governance, Security, Privacy, Legal, Compliance, and Risk teams to make controls, observability, and guardrails scalable and easy to adopt.
Create platform defaults and paved roads that improve speed, reliability, security, governance, and cost efficiency.
Evaluate commercial and open models, infrastructure providers, emerging technologies, and open-source ecosystems; determine where Walmart should build, buy, partner, integrate, or standardize.
Assess emerging AI technologies and distinguish changes that materially affect platform strategy from those that do not.
Build relationships with engineering, product, data science, and business teams; use feedback, telemetry, adoption data, and direct engagement to identify friction and shape the roadmap.
Drive adoption by making the platform valuable to teams and systematically removing barriers to adoption.
Partner with engineering leadership on architecture, APIs, inference, MLOps, orchestration, reliability, observability, capacity, and developer workflows.
Frame complex problems, establish principles, evaluate tradeoffs, make decisions with incomplete information, and move teams from ambiguity to execution.
Communicate the platform strategy and technical concepts clearly to engineers, product teams, executives, and business leaders, linking investments to enterprise strategy and measurable outcomes.
Prioritize foundational investments, customer needs, technology evolution, reliability, technical debt, safety, cost, and strategic bets.
Establish measures for adoption, onboarding, developer productivity, reliability, performance, utilization, cost efficiency, governance coverage, customer satisfaction, and business impact.
Align Product, Engineering, Infrastructure, Governance, Security, Data, Finance, Procurement, and business teams around shared priorities and decisions.
Recruit and develop product leaders and foster a culture of customer focus, technical curiosity, ownership, intellectual honesty, urgency, and high standards.

Benefits

Competitive pay
Performance-based bonus awards
Medical, vision, and dental coverage
401(k)
Stock purchase
Company-paid life insurance
Paid time off, including sick leave
Parental leave
Family care leave
Bereavement leave
Jury duty leave
Voting leave
Short-term and long-term disability
Company discounts
Military Leave Pay
Adoption and surrogacy expense reimbursement
PTO and/or PPTO, with amount based on job classification and length of employment
Live Better U Walmart-paid education benefit for eligible full-time and part-time associates, covering tuition, books, and fees for qualifying programs