Product Manager - Data Collection
Vannevar LabsRemotePosted 11 April 2026
Job Description
Vannevar is a defense technology company building AI to deter our adversaries. In the 21st century, conflict moves at algorithmic speed and foresight equals firepower. Our agentic AI is purpose-built to compete with China—from cross-Strait conflict to gray zone coercion. Trained on the most mission-relevant datasets in defense, our technology models adversary behavior, simulates campaigns, and recommends the best course of action to decision makers. Our AI systems are some of the most trusted in the industry and actively used on the front lines of the Indo-Pacific to keep the peace and save lives.
Exceptional technology starts with exceptional people. Vannevar is a small agile team combining world-class engineers with veteran strategists who bring deep expertise in defense and tradecraft. We’re building a company defined by mission impact, user empathy, and disciplined growth. In just three years, we grew from $3M to $80M in ARR, achieved early profitability, and reached unicorn status—proving that disruption doesn’t require an ego, and staying power doesn’t mean standing still.
About the role
You will be a product lead on our data collection and agent tool calling efforts alongside our Agents and Collection engineering teams. Unique and hard-to-access mission data is a core component of our products. Even more important now is ensuring that data and collection capability is exposed to agents in a usable way via smart tool calling. Tools are our differentiator. You will become in expert in these areas, both on the mission components - what collection matters for which mission workflows, where do we have gaps, where should we invest to fill those gaps - and on the technology components - how do we collect data in a mis-attributable way, how do we build high quality tools for agents to use this data. You will work alongside our users on mission problems, own their outcomes, and set technical investment priorities.
What you'll do
Live with our mission users. Go onsite with our mission team and users to build domain knowledge and your own understanding of the mission problems and workflows our users have.
Master our data and capabilities. We’ve built world-class data collection for some key national security data sets. Understand how we’ve built it, what our differentiators are, and how to take it to the next level.
Scope and prioritize data collection. Turn ambiguous user / mission needs into our data collection strategy in partnership with our customer and engineering (datasets, sources, latency, coverage, etc.) teams. Work with engineering and mission to prioritize and execute these collection efforts.
Collection strategy for agents. Ensure collected data is usable by agents (tool-call readiness, retrieval patterns, schemas/metadata, evaluation loops, etc.). Identify gaps where the right data isn’t getting into agent tool calls and coordinate fixes. Partner with teams to align data and mission workflows as a coherent advantage .
Enable Collection Engineering to move fast. Develop repeatable processes for intake, scoping, execution, launch, and evaluation for data collection requests. Partner with the Collection engineering team to solve other problems.
You might be a fit if you…
Required
Care deeply about the mission: the data we collect is enabling massive, generational impact across many national security missions.
Have strong mission intuition: you can decompose messy operational needs into concrete technical requirements.
Can collaborate deeply with engineers.
Are comfortable making hard prioritization calls with incomplete information.
Communicate crisply: can write a one-pager that aligns stakeholders and drives action.
Preferred
Experience with data acquisition / collection, national security problems and workflows, data platform/product work.
Familiarity with agentic systems and what makes data “tool-call ready”
Prior roles in defense/intelligence mission contexts (or adjacent high-stakes ope ... (truncated, view full listing at source)
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