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.