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Censys

Attack surface management for internet assets

Senior Machine Learning Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeRemote in USA

About the Job

Requirements

  • At least 5 years of experience in data science, machine learning engineering, or software engineering with applied machine-learning responsibilities.
  • Experience building and deploying machine-learning or statistical models in production environments.
  • Experience programming in Go or Python and familiarity with software-engineering practices for building maintainable systems.
  • Experience working with large datasets and building data pipelines for feature generation, training, or inference.
  • Proficiency with supervised and unsupervised learning techniques, including classification, clustering, similarity scoring, or anomaly detection.
  • Ability to evaluate models using sound statistics and understand tradeoffs related to precision, recall, accuracy, and confidence.
  • Ability to write understandable, testable code with an eye toward maintainability.
  • Ability to explain technical concepts, model behavior, and tradeoffs to engineers, researchers, and product managers.

Responsibilities

  • Build and improve machine-learning models and data-driven systems that classify, cluster, label, and enrich Internet-observed assets and services.
  • Own the design and development of applied machine-learning workflows that turn raw Internet telemetry into usable context for internal systems and customer-facing products.
  • Partner with engineering, research, security, and product teams to ensure the right models, datasets, and feedback loops are built to improve coverage and quality.
  • Build parts of the system using experience in machine learning, data science, and software engineering, including feature pipelines, training datasets, model evaluation frameworks, confidence scoring systems, and services that run in the cloud or on-premises.

Desired Qualifications

  • Experience building classification, enrichment, or labeling systems for messy or partially labeled data.
  • Experience deploying models in containerized environments such as Kubernetes.
  • Experience with at least one cloud provider, such as AWS, Azure, or GCP.
  • Familiarity with feature stores, model serving, MLOps workflows, or tools for experiment tracking.
  • Familiarity with security, Internet measurement, or network-derived datasets.

About the Company

Censys

Attack surface management for internet assets

Censys offers an attack surface management platform that provides internet intelligence to security teams. The platform continuously scans the internet to discover and inventory internet-facing assets, giving organizations a complete view of their digital exposure. This helps users identify and remediate risks such as open ports, vulnerable software, and misconfigured devices. Customers include Global 2000 companies, government agencies, and security providers, and the service is sold on a subscription basis. Compared with competitors, Censys emphasizes ongoing, wide-scale internet reconnaissance to produce a comprehensive asset inventory and risk context, not just internal scans or point-in-time checks. The company’s goal is to help organizations manage exposure, reduce security risk, and improve threat detection by providing up-to-date visibility into their attack surface.

Company size
51-200
Company stage
Series D
Total funding
$198.1M
Headquarters
Ann Arbor, MI
Founded
2017

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