Applied AI Engineer

Staff-Level Applied AI Engineer

Vantor is forging the new frontier of spatial intelligence, helping decision makers and operators navigate what's happening now and shape what's coming next. Vantor is a place for problem solvers, changemakers, and go-getters—where people are working together to help our customers see the world differently, and in doing so, be seen differently. Come be part of a mission, not just a job, where you can:
Shape your own future, build the next big thing, and change the world.

To be eligible for this position, you must be a U.S. Person, defined as a U.S. citizen, permanent resident, Asylee, or Refugee.

Export Control/ITAR:
Certain roles may be subject to U.S. export control laws, requiring U.S. person status as defined by 8 U.S.C. 1324b(a)(3).

We are seeking a hands-on Staff-level Applied AI Engineer to build the next generation of decision intelligence within our mission planning systems. This role focuses on learning from historical mission data, quantifying economic outcomes, and improving planning decisions through optimization and machine learning. You will operate as a roving specialist across our engineering organization, working at the intersection of data, algorithms, and operational systems to unlock measurable business impact.

This is not a generic AI role. You will be solving real-world constrained optimization problems with direct revenue implications.

The next generation MPS system doesn't just automate—it needs to both think and act. With AI built in, MPS will function as a digital operator capable of scheduling tasks on specific sensors and autonomously managing and optimizing constellations, while keeping human operators in the loop with auditable controls.

Cybersecurity must be engineered in—not bolted on. The system will align to zero trust principles and modern Dev Sec Ops  practices to accelerate accreditation and deployment across missions. An open architecture and integrated data layer will enable secure collaboration with allies without sacrificing security.

This system must be cloud-native and capable of operating from anywhere. With remote access and seamless integration across terrestrial and space-based networks, operators will compress satellite tasking cycles from days to minutes while maintaining secure operations (potentially) globally.

What You'll Do

• Develop models to attribute revenue to individual collection plans
• Build counterfactual and simulation frameworks to evaluate alternative strategies
• Design decision-support systems used daily by mission planners
• Apply optimization, reinforcement learning, and heuristic approaches
• Map system complexity and identify efficiency improvements
• Rapidly prototype solutions using AI-assisted development tools
• Collaborate across engineering, data, and product teams
• Engineer AI-driven capabilities that enable MPS to act as an autonomous or semi-autonomous operator, balancing automation with human-in-the-loop control
• Design and build systems aligned to zero trust and Dev Sec Ops  principles, ensuring security is foundational—not an afterthought
• Contribute to open architecture and integrated data layer strategies that enable secure collaboration with partners and allied systems
• Build and deploy cloud-native solutions that enable remote mission operations and dramatically reduce planning cycle times

What Success Looks Like (12–18 Months)

• A decision-support system is actively used by planners in daily operations
• Vantor can quantify revenue at the level of individual collection plans
• Teams can evaluate alternative planning strategies with measurable economic outcomes
• Early-stage learning systems (optimization / RL) are improving planning performance over time

Minimum Qualifications

• 5+ years building data-driven or ML-powered systems
• Strong Python or similar programming skills
• Experience with optimization, simulation, or decision systems
• Ability to work in complex problem spaces
• U.S. Citizenship required
• Bachelor's or Master's degree in Computer Science, Applied Mathematics, Operations Research, Engineering, or a related field

Preferred Qualifications

• Experience with operations research or reinforcement learning
• Background in decision…