This role is based in Virginia and sits within an engineering department, which — given the location and organizational context — suggests a company likely operating in or adjacent to the federal technology, defense, or public-sector services space. The organization appears to build software or data-driven products that require rigorous technical and compliance standards, with AI capabilities playing a growing role in how they deliver value. Teams here likely work at the intersection of complex data environments and real stakeholder accountability, where product decisions carry meaningful consequences. If that framing fits, this is a place where thoughtful, evidence-based product leadership matters more than velocity for its own sake.
About the Role
This is a senior individual contributor role for a product manager who has already done the hard work of shipping AI features — not just scoping them — and who knows what it takes to make machine learning capabilities actually useful to real users. You'll own a roadmap that sits at the edge of what's technically possible, working daily with engineering and data science to make judgment calls that balance model performance, user trust, and business outcomes. The impact here is concrete: better AI products, faster, with the rigor that high-stakes environments demand.
Responsibilities
Define and drive the roadmap for AI-powered features from early discovery through launch, establishing clear success metrics and delivering measurable improvements in user outcomes or business performance.
Translate raw technical capabilities — model outputs, APIs, data pipelines — into crisp product requirements and user-facing value propositions that engineering can build and stakeholders can rally around.
Partner daily with data science and engineering to sequence work against real constraints — data quality gaps, model latency, infrastructure cost — keeping the roadmap grounded without letting perfect be the enemy of shipped.
Design and interpret quantitative experiments (A/B tests, funnel analyses, feature-level instrumentation) to validate AI product decisions, surface underperformance early, and drive iterative improvement post-launch.
Lead cross-functional alignment on responsible AI tradeoffs — accuracy vs. fairness, explainability vs. complexity, speed vs. compliance — bringing engineering, legal, design, and ops to durable decisions rather than prolonged debates.
Own the product narrative for AI capabilities with internal and external stakeholders, communicating what the system does, what it doesn't, and why those boundaries exist.
Identify gaps in how the team discovers, specs, and ships AI features, and actively improve the process — not just execute within it.
What we're looking for
Has taken AI or ML-powered product features from discovery through launch, with documented user or business impact — not just participated, but owned the outcome.
Has a demonstrated track record of turning ambiguous technical capabilities (models, APIs, data pipelines) into requirements teams could build and value propositions users could understand.
Has worked shoulder-to-shoulder with engineering and data science to prioritize under real-world constraints — data limitations, model brittleness, latency ceilings, budget — and made defensible calls when tradeoffs were uncomfortable.
Has used quantitative methods — A/B testing, funnel analysis, cohort tracking, or similar — to make and validate product decisions, and can point to features that improved because of that rigor.
Has successfully aligned cross-functional stakeholders, including legal and compliance voices, on responsible AI tradeoffs such as model fairness, output explainability, and acceptable error rates — moving groups from disagreement to decision.
Communicates with technical and non-technical audiences equally well, adjusting depth and framing without losing accuracy or credibility.
Operates with a high degree of autonomy — can set direction, manage ambiguity, and make sound calls without waiting to be told what matters.
Nice to have
Experience working in a government, defense, or federal contracting environment, with an understanding of how procurement cycles, clearance considerations, or regulatory oversight shape product scope and timelines.
Has shipped production-scale features involving NLP, computer vision, or LLM-based capabilities — familiar with the gap between demo performance and production reliability.
Has contributed to AI governance artifacts such as model cards, responsible AI checklists, or model-risk documentation, and understands why they matter beyond checkbox compliance.
Comfortable enough with prompt engineering or fine-tuning workflows to engage technical teams in substantive conversations — not just relay requirements, but pressure-test assumptions.
Has helped grow junior product managers or shaped PM team practices, whether through mentorship, documentation, or building repeatable processes others could follow.
Benefits
Competitive base salary benchmarked to senior PM market rates in the Virginia/DMV corridor, with performance-based incentives.
Hybrid work model that gives you real in-person collaboration without requiring you to be on-site every day.
Direct access to consequential, technically complex work — the kind where your product decisions have real downstream stakes and visible outcomes.
Opportunity to help shape how AI is built and governed within the organization, not just execute someone else's vision for it.
Comprehensive benefits package including health, dental, and vision coverage, plus retirement contributions.
A team environment where technical depth is respected and product managers are expected to have opinions, not just manage backlogs.
