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About 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.
Responsibilities
- 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.
Requirements
- 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.
- Clear communication of trade-offs to engineers and customers.
- Experience with adversarial ML, safety or content-moderation models.
- Published work on evaluations or AI security.
Skills
- Python
- PyTorch
- NLP
- LLM
- Machine Learning
- Data Science
- Adversarial ML
- AI Security
Location
- Remote
Work Type
- Full-time
Experience Level
- 5+ years
Salary/Compensations
- £85,000–£145,000
Benefits
- Competitive equity to share in Refractal's growth
- Pension contribution
- Private medical and dental cover
- Visa and relocation support
About the Company
- Refractal is an AI security company building the infrastructure organisations need to defend against autonomous AI threats.
- 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.
- Radical transparency. We believe the best work happens when everyone feels able to give and receive honest, constructive feedback.
- Public service. We see AI security as a form of public service. Getting the AI transition right means putting the right infrastructure in place to manage the threats that come with it, and our work helps protect the institutions and services society depends on.
- Fail fast. We test ideas early, seek feedback and learn quickly. We experiment rapidly and change course when something isn't working, without compromising on security.
- Pragmatism, not dogmatism. We choose approaches based on how well they solve the problem. That means combining established cybersecurity methods with frontier AI research, questioning our assumptions, and changing our minds when the evidence changes.