Adversarial ML Engineer for Cybersecurity & Production

Greenhouse Software, Inc.; Greater London, Manchester, Bath; Hybrid; GBP 70,000 - 110,000; Full time

Job summary

Netcraft is seeking an ML engineer to develop adversarial classifiers that block cyber threats. You will deploy models on GPU-backed Kubernetes clusters, integrate vLLM-based inference, and collaborate with engineers and analysts to improve detection at scale.

You’ll train and evaluate models, run experiments, and monitor performance in production, adapting approaches as attack patterns evolve. The role emphasizes pragmatic ML and reliable systems in a security-focused environment.

Qualifications

• Proficiency with web fundamentals (HTTP, DNS, how the web works).
• Understanding of ML training, evaluation, and precision/recall concepts.
• Strong written and verbal communication; ability to articulate impact clearly.
• Empirical mindset: design experiments, measure outcomes, use metrics.
• Willingness to learn Go and Perl (async programming).
• Attention to detail and accountability for production classifications.

Responsibilities

• Build, evaluate, and tune classifiers for blocking decisions.
• Deploy and operate model serving infra on Kubernetes, including LLM inference.
• Optimize models for high-volume, low-cost serving.
• Run experiments on live data; measure precision, recall, false positives.
• Integrate models into production classification platforms.
• Create and maintain pipelines for features, retraining, and metrics.

Skills

Web fundamentals

ML fundamentals

Communication skills

Empirical mindset

Go & Perl learning

Attention to detail

Tools

Kubernetes

Flux

Argo

API gateways

LLM inference (vLLM)

MLflow

Argo Workflows

llama.cpp

SQL databases

Job description

Netcraft is seeking an ML engineer to develop adversarial classifiers that block cyber threats. You will deploy models on GPU-backed Kubernetes clusters, integrate vLLM-based inference, and collaborate with engineers and analysts to improve detection at scale.

You’ll train and evaluate models, run experiments, and monitor performance in production, adapting approaches as attack patterns evolve. The role emphasizes pragmatic ML and reliable systems in a security-focused environment.