Research Engineer, AI (Entry-Level)

About the Role

Support applied research across Orbis AI focus areas: agentic AI, knowledge representation, dynamic ontologies, autonomous analysis, and high-performance analytics. Work alongside senior research engineers on questions with real product stakes. This role is designed to bring strong early-career engineers into applied AI research and grow them toward owning a research area.

Key Responsibilities

• Reproduce baselines and published methods, and document precisely where results diverge.
• 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 for others to rerun.
• Perform error analysis—characterize system errors and their causes, and report them directly.
• Implement well-scoped prototype components with design direction and code review from senior engineers.
• Summarize relevant literature and open-source work, explaining their relevance to the task.
• Write up results, including negative findings, and present them in team research reviews.
• Travel occasionally, up to 10%, for conferences, training, and team collaboration.

Requirements & Tech Stack

• Solid programming fundamentals in Python.
• Working understanding of machine learning and LLM fundamentals (evaluation basics, prompting, embeddings, retrieval).
• Ability to read a technical paper and explain its claims, evaluation methods, and limitations.
• Intellectual honesty regarding results, even when they contradict a hypothesis.
• Willingness to travel occasionally, up to 10%.

Qualifications

• Bachelor's or Master's degree in Computer Science, Machine Learning, or a related technical field, or equivalent demonstrated ability.
• Research experience from a thesis, lab, internship, or open-source project is desired.
• Hands-on project experience with retrieval-augmented generation, agent frameworks, or fine-tuning is desired.
• Exposure to graph databases, ontologies, or information extraction is desired.
• Experience with experiment tracking and evaluation tooling (e.g., notebooks, dataset construction, scoring pipelines) is desired.
• Exposure to containerized environments (Docker) and cloud or GPU compute is desired.
• Interest in OSINT, intelligence, or investigative research workflows is desired.

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 specified work regions.