Research Engineer, AI; Entry-Level

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.

Support applied research across the Orbis AI focus areas - agentic AI, knowledge representation, dynamic ontologies, autonomous analysis, and high-performance analytics - working alongside senior research engineers on questions with real product stakes. You will reproduce baselines, build evaluation datasets and harnesses, run experiments, and write up what actually happened. This role exists to bring strong early-career engineers into applied AI research and grow them toward owning a research area.

Travel
:
Occasional, up to 10%, for conferences, training, and team collaboration.

• Reproduce baselines and published methods, and document precisely where your results diverge from what was reported.
• Build and maintain evaluation datasets and harnesses, including cases that capture known failure modes.
• Run experiments designed with senior research engineers, keeping records complete enough that someone else can rerun them.
• Perform error analysis - characterize what the system got wrong and why, and report it without smoothing it over.
• Implement well-scoped prototype components with design direction and code review from senior engineers.
• Summarize relevant literature and open-source work, and explain how it bears on the question at hand.
• Write up results, negative results included, and present them in team research reviews.
• Travel occasionally, up to 10%, for conferences, training, and team collaboration.

• 0-2 years of professional experience
• Bachelor's or Master's degree in Computer Science, Machine Learning, or a related technical field, or equivalent demonstrated ability
• Solid programming fundamentals in Python, with code you can walk us through and defend
• Working understanding of machine learning and LLM fundamentals - evaluation basics, prompting, embeddings, and retrieval - from coursework, projects, internships, or professional work
• Ability to read a technical paper and explain what it claims, how it was evaluated, and where it might not hold
• Intellectual honesty about results, including the ones that undercut a hypothesis you liked
• Willingness to travel occasionally, up to 10%

• Research experience from a thesis, lab, internship, or open-source project
• Hands-on project experience with retrieval-augmented generation, agent frameworks, or fine-tuning
• Exposure to graph databases, ontologies, or information extraction
• Experience with experiment tracking and evaluation tooling such as notebooks, dataset construction, or scoring pipelines
• Exposure to containerized environments (Docker) and cloud or GPU compute
• Interest in OSINT, intelligence, or investigative research workflows

• 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
  :
  Occasional, up to 10%, for conferences, training, and team collaboration.

Beyond the opportunity to work alongside practitioners…