Research Engineer
Posted 15 Sep 2026
Advertised as “Research Engineer - Applied ML & AI”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Contract
- Part-time
No salary listed for this role.
Field of studyComputer Science, Computer Vision, Data Science, Machine learning, Natural Language Processing (or related)
Study levelDoctorate
Since 2004 Tomax has created cutting-edge digital solutions for higher education.
We specialize in the end-to-end management of the entire exam and assessment lifecycle.
Our software empowers universities and colleges to efficiently, effectively, and expertly prepare, manage, grade, review, control, and secure the exam and assessment process.
In this role, you will work directly alongside our CTO and engineering team on active production systems serving tens of thousands of students worldwide. Your work will directly shape our core product, blending immediate real-world impact with rigorous research depth.
The role focuses on, but is not limited to, two primary tracks:
1. Exam Integrity & Behavioral Analysis: The core research challenge is evolving from isolated signal detection to unified behavioral analysis-fusing multiple data streams into a coherent, explainable integrity model (Explainable AI).
2. AI for Educational Assessment & Analytics: We are looking for a researcher to uncover insights and drive high-impact AI implementations.
Requirements
- Current PhD student in Computer Science, Data Science, Machine Learning, Computer Vision, NLP, or a related quantitative field.
- Strong software engineering fundamentals and practical, hands-on ML experience.
- Demonstrated research autonomy: ability to formulate hypotheses, design and run experiments, and communicate findings effectively.
- Comfort working with noisy, unstructured, real-world data
- Background in video/audio processing, behavioral modeling, or anomaly detection.
- NLP expertise, particularly with Hebrew or multilingual domain models.
- Exposure to EdTech, psychometrics, or learning analytics.
- Familiarity with cloud infrastructure (AWS).
- Naturally curious and proactive; able to formulate hypotheses and test ideas independently.
- Bridges academic depth with product reality; comfortable navigating noisy, real-world data.
- Agility & Self-Management- Skillfully context-switches between PhD demands and engineering team paces.
- Entrepreneurial mindset to uncover hidden insights in datasets and propose new features.
- Articulate and collaborative; simplifies complex AI concepts for the CTO and dev team.