Company Overview

Orbis is a technology company that delivers sovereign intelligence and decision capabilities to the United States Government and its allies. We convert complex information and operational systems into decisive outcomes, giving public institutions and critical enterprises the tools to move faster, see clearer, and act first in an increasingly contested world.

We are not a consulting firm and not a systems integrator. Every Orbis team is built around practitioners who have operated at the mission edge, and every engagement is judged on one thing: whether it produces working capability, not slide decks. Solutions forged from experience; that is how we build, and it is how we hire.

Job Summary

Lead applied research in one or more Orbis AI focus areas — agentic AI, knowledge representation, dynamic ontologies, autonomous analysis, and high-performance analytics — and carry the results into the product. You will frame the problem, survey what already exists, build prototypes that test a real hypothesis, and measure honestly enough to act on the answer. The role exists because our advantage comes from turning advanced research into dependable mission capability.

Travel: Up to 20% for conferences, customer engagement, and team collaboration.

Key Responsibilities

Frame research problems drawn from real product and mission gaps, define what a result must show to be worth productizing, and survey existing work before building.
Build prototypes that isolate the question being asked, at the fidelity the question actually requires.
Run rigorous experiments — baselines, ablations, held-out evaluation, and honest error analysis — with methodology another engineer can reproduce.
Build and maintain the datasets and evaluation harnesses your area depends on, including cases that capture known failure modes.
Report results clearly, negative results included, and make a defensible recommendation to continue, pivot, or stop.
Transition validated results into production alongside product engineers, staying involved through the handoff rather than delivering a notebook.
Contribute to the scientific community through publications, open-source releases, and academic collaborations, and advise Orbis on what in the field is worth adopting.
Travel up to 20% for conferences, customer engagement, and team collaboration.

Required Qualifications

3+ years of applied research or research-engineering experience, or an M.S./Ph.D. in Computer Science, Machine Learning, or a related field with equivalent applied work
Strong Python engineering skills, producing research code another engineer can read, run, and build on
Demonstrated experience designing and running experiments that produced an actionable conclusion, with methodology you can defend
Depth in at least one Orbis AI focus area: agentic AI, knowledge representation, dynamic ontologies, autonomous analysis, or high-performance analytics
Working knowledge of modern LLM and retrieval methods and their practical limitations
Clear technical writing that makes a result legible to engineers and non-specialist stakeholders alike
Willingness to travel up to 20%

Desired Qualifications

Publication record, open-source contributions, or academic collaborations in a relevant area
Experience carrying a research result all the way into a production system
Experience with knowledge graphs, ontology engineering, reasoning systems, or information extraction
Experience with agent architectures, planning, or multi-agent coordination
Experience with model training, fine-tuning, or evaluation at scale
Experience with performance engineering on data-intensive or analytical workloads
Familiarity with OSINT, intelligence, or investigative research workflows

Physical Requirements

Prolonged periods of sitting at a desk and working on a computer
Participation in virtual and in-person meetings
Ability to attend planned meetings and/or work in classified spaces for extended periods within the specified work regions
Travel: Up to 20% for conferences, customer engagement, and team collaboration.