Senior Security Data Engineer

Censys

4d•Remote

About The Position

Censys is seeking a Senior Security Data Engineer to analyze large-scale Internet data and contribute to building features, labels, and datasets for security-focused Artificial Intelligence (AI) / Machine Learning (ML) models. This role involves transforming raw Internet telemetry into enriched, model-ready data to improve the classification and understanding of hosts, services, and infrastructure across the Internet. The primary focus is on supporting systems that provide labeled security data for classifying entities as benign, suspicious, or malicious, thereby enhancing AI-powered solutions for Security Operations Center (SOC) initiatives. The position requires an ownership mindset, curiosity, mentorship capabilities, and a collaborative spirit, focusing on the context and data transformation aspects rather than direct SOC analysis, threat hunting, or rapid response.

Requirements

5+ years of experience in research or software engineering with a security data focus
Experience analyzing large datasets and turning ambiguous data into usable information that solves security problems
Strong programming skills in Go/Python (or similar), plus experience using SQL (or similar) tools to analyze large datasets
Strong communication skills and the ability to work effectively with researchers, ML engineers, software engineers, and other security experts
Comfort working with security-relevant data and applying technical judgment to questions about Internet-exposed hosts, services, and infrastructure

Nice To Haves

Experience building feature pipelines, labeling systems, or training datasets for security, fraud, reputation, or risk scoring models
Experience with Internet measurement, asset intelligence, network security, or large-scale telemetry analysis
Familiarity with common Internet protocols and technologies such as HTTP, TLS, DNS, SSH, SMTP, and PKI/certificates
Familiarity with ML techniques such as classification, clustering, similarity scoring, anomaly detection, or data science in general
Understanding of how attackers, exposed services, misconfigurations, and infrastructure patterns can manifest in Internet-scale data

Responsibilities

Analyze Censys telemetry and derived datasets to identify signals that improve AI/ML model training for classification that affects security outcomes
Build and improve training and evaluation datasets using Internet telemetry, manually curated labels, and analyst-reviewed data
Drive feature discovery, feature selection, and labelling strategies for models that classify entities as benign, suspicious, or malicious
Work on multi-layer labeling and classification problems, where categories such as device type, router, honeypot, or edge service may need to be identified before risk classification
Partner with Research / Detection teams to translate security domain expertise into actionable workflows
Collaborate with ML engineers and software engineers to ensure features, labels, and model inputs are practical to productionize
Contribute to feedback loops and evaluation frameworks that improve precision, recall, confidence, and coverage over time
Build tooling to support the efforts listed above