Research Engineer, AI (Entry-Level)

About the Role

Support applied research across the 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. The role involves reproducing baselines, building evaluation datasets and harnesses, running experiments, and documenting results. This position 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, documenting divergences from reported results.
• Build and maintain evaluation datasets and harnesses, including cases that capture known failure modes.
• Run experiments designed with senior research engineers, maintaining complete records for reproducibility.
• Perform error analysis to characterize system failures and their causes.
• 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, with demonstrable code.
• Working understanding of machine learning and LLM fundamentals (evaluation basics, prompting, embeddings, and retrieval).
• Ability to read a technical paper and explain its claims, evaluation methods, and limitations.
• Intellectual honesty regarding research results.
• Willingness to travel occasionally, up to 10%.

Qualifications

• 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.
• Travel: Occasional, up to 10%.

Desired Qualifications

• 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 (e.g., notebooks, dataset construction, scoring pipelines).
• Exposure to containerized environments (Docker) and cloud or GPU compute.
• Interest in 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 specified work regions.
• Travel: Occasional, up to 10%.