Data & AI Evaluation Engineer for Security ML Pipelines

Menlo Security; United Kingdom; Hybrid; GBP 60,000 - 90,000; Full time

Job summary

Menlo Security in the United Kingdom is seeking a Data & AI / ML Evaluation Engineer to own data acquisition, curation, and evaluation infrastructure for our AI security products. You will build automated data ingestion pipelines from surrogate web traffic and open sources, manage labelling workflows, and train models.

Collaborate with research and software teams to transform telemetry into actionable model improvements, and contribute to benchmarking, uptime, and throughput across threat

Qualifications

• 2–4 years of experience in software engineering, data engineering, or automated QA/evaluation roles.
• Strong programming in Python; additional in C++, SQL is a plus.
• Experience with model training and ML/AI algorithms.
• Experience building data pipelines, web scrapers, or automated data processing systems.
• Familiarity with data structures, storage formats, and database configurations.
• Methodical approach to dataset labeling, validation, bug logging, and metric tracking.

Responsibilities

• Design and operate automated data collection, mining, and web-scraping pipelines to gather threat samples.
• Build data processing, annotation, and dataset curation workflows and train/re-train models.
• Develop benchmarking and evaluation harnesses to test model detection quality and efficiency.
• Create pipelines for model training and collaborate with research/engineering teams to drive improvements.

Skills

2-4 years experience

Python

ML/AI algorithms

Dataset labeling & metrics

Tools

C++

SQL

Docker

Terraform

Kubernetes

Linux

AWS / GCP

Job description

Menlo Security in the United Kingdom is seeking a Data & AI / ML Evaluation Engineer to own data acquisition, curation, and evaluation infrastructure for our AI security products. You will build automated data ingestion pipelines from surrogate web traffic and open sources, manage labelling workflows, and train models.

Collaborate with research and software teams to transform telemetry into actionable model improvements, and contribute to benchmarking, uptime, and throughput across threat