As a Manager of Program Management on the Trust Sensitive Content team, you will shape how Alexa protects hundreds of millions of customers from harmful content using generative AI and responsible AI guardrails.
About the Role
You start with the customer and work backwards — every program decision is anchored in customer trust and experience.
This Manager, Program Management role blends strategic leadership with program management excellence. You will define vision, lead cross-functional program delivery, and build high-performing teams in an environment where problems are ambiguous, stakes are high, and innovation moves at pace.
This is not a role for someone who follows playbooks — it is a role for someone who writes them. Four things set the right candidate apart above all else:
- Demonstrated Program Management — you have led large, complex, cross-functional programs end-to-end and have the track record to prove it
- Invent & Simplify — you don't manage complexity, you reduce it. You build scalable solutions where ambiguity existed before
- Mechanisms — you build repeatable, self-sustaining operating systems that scale without requiring heroics
- Data & LLM Fluency — you understand how large language models work, where they fail, and how data quality decisions upstream shape model behavior and customer outcomes downstream
Key job responsibilities Strategic Program Leadership (Ambiguity & Scope)
- Define and execute strategic roadmaps for responsible AI programs — working backwards from customer problems, safety requirements, and regulatory needs
- Translate high-ambiguity programs across AI quality, data integrity, and content safety into actionable plans with clear success metrics
- Negotiate priorities, secure resources, and influence stakeholders across engineering, legal, science, and policy to deliver program value
- Bring data and LLM awareness to strategic decisions — connect generative AI model behavior, data pipelines, and evaluation frameworks to customer outcomes
- Program Execution & Operational Excellence (Execution & Problem Complexity)
- Own end-to-end delivery of multiple cross-functional programs simultaneously — build release schedules, manage dependencies, and mitigate risks proactively
- Define and monitor success metrics (quality rates, audit pass rates, customer satisfaction signals) and report progress in Leadership Reviews to executive stakeholders
- Build mechanisms — establish SLAs, audit frameworks, and operating workflows that drive accountability and long-term operational excellence
- Use metrics to challenge assumptions, surface insights, and make the case for course corrections; understand the data quality and LLM evaluation signals that indicate program health
- Team Building & Program Governance (People & Team Management)
- Lead a team of program and compliance associates; recruit bar-raising talent, create structured onboarding plans, and mentor ICs toward technical excellence and expanded scope
- Govern program health through regular risk assessments, blocker tracking, and re-prioritization to balance short-term deliverables with long-term innovation goals & establish high standards for program documentation, decision-making, and execution discipline across the team
- Empower teams to solve problems autonomously, recognize contributions, and maintain sustainable workloads
- Cross-Functional Program Influence (Scope & Influence)
- Partner with engineering, data science, and policy teams to align roadmaps and resolve trade-offs (speed vs. accuracy, scalability vs. compliance, data richness vs. latency)
- Champion program outcomes in strategic forums (MBR/QBR, OP1/OP2), articulating technical and business impacts to leaders up to L10
- Advocate for reusable, scalable solutions that avoid reinvention and accelerate time-to-value for safety and quality programs
- Engage with science and engineering teams on LLM behavior, data pipeline quality, and model evaluation design — bridge the gap between technical execution and program delivery
- Build trust and alignment without direct authority in a matrixed organization
- Communication & Stakeholder Management (Communication & Impact)
- Write clear, data-driven documents (6-pagers, program narratives, WBR decks) that align teams and secure buy-in from senior leaders
- Resolve contentious issues by harmonizing diverse viewpoints — engineering feasibility, policy compliance, customer experience — through data-driven discussions
- Maintain transparency via dashboards, status reports, and cross-team syncs to ensure accountability and agility
A day in the life No two days look the same — but every day centers on protecting customer trust at scale. You move between strategy and execution, connecting the dots across science, engineering, and policy to deliver responsible AI programs that work.
- Develop and iterate on scalable solutions for emerging content safety challenges — from defining evaluation frameworks for new LLM capabilities to designing guardrail mechanisms that reduce manual intervention
- Lead weekly program reviews with engineering and science leads to track delivery, surface risks, and unblock teams
- Write and present program narratives for MBR/QBR and leadership reviews that connect program health to customer outcomes
- Partner with Applied Science to translate model behavior insights into actionable program requirements — closing the loop between data quality, model performance, and customer experience
- Triage and resolve cross-team blockers across engineering, policy, and science partners — making trade-off decisions that balance speed, accuracy, and compliance
- Review program metrics dashboards and drive course corrections based on data signals — not waiting for problems to surface, but building the mechanisms that catch them early
About the team The Trust Sensitive Content and Feedback Intelligence organization pioneers the protection of customer trust in Alexa and Devices by identifying and mitigating sensitive content across text, image, video, and audio. We combine generative AI, machine learning, and responsible AI guardrails to deliver real-time safety solutions at scale.
We are a fast-moving, highly collaborative team that partners closely with Applied Science, Engineering, and Policy teams. We don't just run programs — we invent the frameworks that make them run. If you thrive in ambiguity, love building from scratch, and want your work to directly shape the safety and quality of AI experiences for millions of customers, this is the team for you.
Basic Qualifications
- 7+ years of program or project management experience
- 2+ years of team management experience
- 5+ years of working cross functionally with tech and non-tech teams experience
- Experience defining program requirements and using data and metrics to determine improvements
- Proven ability to lead in ambiguity — define vision, set metrics, and drive outcomes where the problem is undefined and the path must be invented
- Working knowledge of data systems, data quality principles, or AI/ML program delivery — enough to engage credibly with science and engineering teams
- Expertise in building and managing high-performing teams, including hiring, mentoring, and performance management
- Strong written and verbal communication skills; experience presenting to senior leadership (Director/VP level)
- Bachelor's degree in a quantitative or technical field (e.g., computer science, engineering, data science, statistics, mathematics, research), or equivalent experience.
Preferred Qualifications
- Experience managing AI/ML programs — generative AI safety, LLM evaluation, model quality, responsible AI guardrails, or equivalent
- Data fluency — ability to define metrics, interrogate data, and use quantitative signals to drive program decisions and surface risks
- Familiarity with how large language models work — training data, evaluation benchmarks, model behavior, failure modes, and the role of data quality in model outcomes
- Demonstrated ability to Invent & Simplify in practice — examples of reducing operational complexity, creating clarity from ambiguity, and driving adoption of new mechanisms
- Familiarity with Amazon's Leadership Principles and matrixed program delivery
- Experience consolidating fragmented programs into coherent, scalable structures
- Ability to balance tactical execution with strategic vision — eliminating bottlenecks while keeping teams focused on long-term goals
- Experience working with Applied Science and Engineering teams in high-stakes environments
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