Research Engineer, AI

Orbis

1d

About The Position

Lead applied research in one or more Orbis AI focus areas: agentic AI, knowledge representation, dynamic ontologies, autonomous analysis, and high-performance analytics. The role carries research results into products by framing problems, surveying existing work, building hypothesis-testing prototypes, and rigorously measuring results. The purpose of the role is to turn advanced research into dependable mission capability.

The position works alongside product engineers to transition validated results into production and remains involved through the handoff. It also contributes to the scientific community through publications, open-source releases, and academic collaborations, and advises Orbis on relevant developments in the field. Travel is up to 20% for conferences, customer engagement, and team collaboration.

Orbis works in arrangements determined by mission needs. Depending on the role, work may be customer-site, office-based, hybrid, or fully remote; the posting does not specify which arrangement applies to this role. The position may involve working in classified spaces within specified work regions.

Requirements

• At least 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, including producing research code that other engineers can read, run, and build on.
• Demonstrated experience designing and running experiments that produced an actionable conclusion, with defensible methodology.
• 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 results understandable to engineers and non-specialist stakeholders.
• Willingness to travel up to 20%.
• Ability to attend planned meetings and/or work in classified spaces for extended periods within specified work regions.

Nice To Haves

• 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.

Responsibilities

• Frame research problems based on product and mission gaps, define what results would justify productization, and survey existing work before building.
• Build prototypes at the level of fidelity required to test the research question.
• Run rigorous experiments, including baselines, ablations, held-out evaluation, and error analysis, using reproducible methodology.
• Build and maintain relevant datasets and evaluation harnesses, including cases that capture known failure modes.
• Report results clearly, including negative results, and recommend whether to continue, pivot, or stop.
• Transition validated results into production alongside product engineers and remain involved through handoff.
• Contribute to the scientific community through publications, open-source releases, and academic collaborations.
• Advise Orbis on developments in the field that are worth adopting.
• Travel up to 20% for conferences, customer engagement, and team collaboration.

Benefits

• Competitive retirement matching.
• PTO policy that encourages real time off.
• Comprehensive benefits package for employees and their families; the full list is shared with candidates following an initial conversation with HR.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed