Data Scientist

Refractal; Greater London; On-site; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Visa sponsorship

Relocation support

Job summary

Refractal, an AI security company, is seeking an experienced ML/Data Scientist to own machine-learning models behind detections in production. You will train, tune and ship classifiers addressing prompt injection, data exfiltration, and policy violations, while designing low-latency models and evaluating performance across customers.

You will analyze production telemetry for drift and false positives, build benchmarks, and collaborate with security engineers to convert new attacks into training

Qualifications

• 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.

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.

Skills

Applied ML / data science

Python

PyTorch

Statistics

NLP

LLM

Fine-tuning

Embeddings

Classification

Model evaluation

Communication

Equity awareness

Job description

Visa sponsorship / work permit support is mentioned in the posting. Relocation support is mentioned in the posting.

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.

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.

• 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.
• Experience with adversarial ML, safety or content-moderation models.
• Published work on evaluations or AI security.

Our values

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.

• 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.
• Competitive equity to share in Refractal's growth