Senior Data Scientist

About this role

About Refractal Refractal is an AI security company building the infrastructure organisations need to defend against autonomous AI threats. 2026 has shown that generally capable intelligence is here. The challenge now is modernising security to stop AI-enabled threats, whether they come from internal agents going rogue or from attackers using AI to become more dangerous. Refractal was founded on cybersecurity and AI expertise from MIT, Microsoft, NASA and the U.S.

Navy. Our product lets organisations detect AI-enabled threats at low cost without sacrificing detection quality. We take a practical approach to AI risk, combining proven cybersecurity methods with frontier AI security research. Today, we work with governments and organisations that can't afford to get security wrong. We're backed by leading deep-tech and cybersecurity investors and are growing the team to deliver on this mission.

The role You'll own the machine-learning models behind Refractal's detections in production, which decide in milliseconds whether an action is safe, and the analysis that shows how well they perform for each customer. What you'll do Train, tune and ship classifiers for prompt injection, data exfiltration, policy violations and other risky AI activity. Design low-latency models, including distilled and small models and rules-plus-ML ensembles.

Analyse production telemetry for drift, blind spots, false-positive cost and new attack patterns. Build evaluations and benchmarks for our detection models, and work with security engineers to turn new attacks into training data. Turn company policies and regulatory requirements into measurable controls. Success in your first six months Detection models you own are in production, with quality metrics tracked per customer.

You've reduced false positives or latency measurably without losing detection quality. Production data feeds a regular cycle of model improvements. About you: essential 5+ years in applied ML or data science, with models shipped to production. Strong Python, PyTorch or similar, and solid statistics for evaluation. Hands-on NLP and LLM work: fine-tuning, embeddings, classification and LLM-as-judge. Experience measuring fuzzy problems, including labelling, agreement and drift.