Applied Data Scientist — Intent Drift Detection (AI)

Location: Tel Aviv-Yafo, Israel

Company

hirify.global builds security protection and visibility for AI-driven software development workflows, including developer computers and IDE-based AI interactions.

What you will do

• Own intent-drift detection for AI coding agents end to end, from dataset creation through model deployment.
• Design and generate data defining intent drift and co-design the data-generation pipeline.
• Fine-tune, distill, and reduce models for realtime, fully on-device inference.
• Define the balance between models, rules, and gates in the detector architecture.
• Build evaluation harnesses focused on realtime detection and very low false-positive rates.

Requirements

• 3–5 years of applied data science experience covering problem framing, data collection and labeling, feature or representation design, training, and evaluation.
• Experience owning a dataset and the model that consumes it.
• Fluency with small open models, including DeBERTa-class encoders, models up to 8B parameters, LoRA, quantization, and single-GPU workflows.
• Strong practical applied-research mindset and ability to work independently on an open problem.
• Local-first development experience with Python, PyTorch, and Hugging Face.
• Startup-oriented, collaborative, and execution-focused approach.

Nice to have

• Security background in prompt injection, MITRE ATLAS, or agent security.
• Experience with LLM evaluation, red-teaming, or adversarial dataset development.
• Familiarity with endpoint software running locally on developer machines with low resource usage.

Culture & Benefits

• R&D role focused on an open problem in AI-agent security.
• Local-first product design with no cloud requirement for the detection models.
• High ownership over the problem, dataset, model, and detector architecture.