Modeling and Simulation Lead

Onebrief builds collaboration and AI-powered workflow software for military planning and operational coordination.

Military planning is complex by nature, requiring teams to coordinate information, people, and decisions across systems and locations. Onebrief brings planning, collaboration, simulation, and AI into one connected environment, helping teams test strategies, adapt to changing conditions, and make decisions with greater clarity when the stakes are real.

We are a distributed team of builders from military, operational, and technology backgrounds who care deeply about improving how important work gets done. Some team members work remotely, while others work directly alongside customers in operational environments around the world.

Founded in 2019, Onebrief is backed by leading investors including General Catalyst, Battery Ventures, Insight Partners, Sapphire Ventures, and Human Capital. Valued at more than $2 billion, we continue to invest in product innovation, AI capabilities, and team growth.

Military planners need to understand what a simulation can tell them, where its assumptions hold, and where they do not. At Onebrief, you will own the models and data that make those answers defensible in Atom Engine, our cloud-based simulation engine for military planning.

You will lead the technical standards and production workflows behind Atom Engine's entity catalog: how military platforms and capabilities are represented, which sources and formulas support their performance, and how that work is tested. Your judgment will shape the catalog that scenarios run on and the methods other analysts use to build it.

As a Staff-level individual contributor, you will set standards and mentor other analysts. You will stay hands-on with research, modeling, and validation while directing AI agents to scale first-pass research, data entry, documentation, and testing. You will define tooling and test requirements with Engineering and partner on implementation. Production software development is not the primary focus.

• Modeling standards. Research and select credible sources. Adopt, adapt, or develop defensible formulas for weapon and sensor effects, fuel consumption, survivability, and other performance characteristics. Document assumptions, uncertainty, and the conditions under which a model can be trusted.

• Catalog priorities. Maintain a roadmap across service branches, nations, and eras. Work with Customer Relations to prioritize the catalog segments customers need most, and track coverage, freshness, and confidence so the team knows what to build or improve next.

• AI-directed production. Design the instructions, review checkpoints, and quality controls that analysts and AI agents use to create entities iew their work, correct errors, and turn successful methods into repeatable workflows. Define requirements for tools that improve production.

• Validation and testing. Lead verification, validation, and accreditation (VV&A) across catalog segments. Use source evidence and subject matter expert (SME) review to assess model credibility; partner with Engineering on continuous automated tests. Run competitive wargames with colleagues to expose gaps, challenge assumptions, and guide corrections.

• Technical judgment with customers. Build a network of SMEs and resolve contested modeling decisions with customers and internal teams. Explain the evidence behind the numbers, make limitations clear, and use feedback to improve the catalog.

• Shared working practices. Author standards and knowledge articles that others can apply consistently. Train and mentor analysts in research methods, modeling judgment, AI workflows, and VV&A so the catalog…