Machine Learning Engineer (AI/ML) — United States

About the Company

Censys is a cybersecurity company focused on providing comprehensive, accurate, and up-to-date intelligence about the internet. Its mission is to create a reliable map of the internet that helps organizations understand their digital assets, exposure, and security risks.

The company delivers real-time internet intelligence and actionable threat insights to global governments, more than 50% of Fortune 500 companies, and leading threat intelligence providers worldwide. Its platform uses IP scanning, DNS lookups, web crawling, and certificate data to build a detailed view of internet-connected assets and services.

Founded by security researchers, Censys develops technology that helps individuals and organizations gain insights into their digital environments as the internet, IoT, and cloud technologies continue to evolve. The company has Midwestern roots and operates as a security-focused startup.

About the Role

Censys is seeking a Senior Machine Learning Engineer to develop machine learning models and data-driven systems that classify, label, and enrich large volumes of internet data. This role focuses on transforming raw internet telemetry into high-quality datasets, classifications, and insights that support internal systems and customer-facing products.

You will contribute to the development of Censys's data platform, helping create richer context and identify complex relationships across internet-observed assets and services. The position involves designing and developing applied machine learning workflows while collaborating with engineering, research, security, and product teams to improve data coverage and quality.

Key Responsibilities

• Build and improve machine learning models and data-driven systems to classify, cluster, label, and enrich internet-observed assets and services.

• Design and develop applied machine learning workflows that transform raw internet telemetry into useful context for internal systems and customer-facing products.

• Collaborate with engineering, research, security, and product teams to identify and develop suitable models and datasets.

• Build feedback loops to improve model coverage and data quality.

• Develop feature pipelines and training datasets.

• Build model evaluation frameworks and confidence-scoring systems.

• Develop services that operate in cloud or on-premises environments.

• Apply machine learning, data science, and software engineering expertise across different components of the data platform.

Required Qualifications

• 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.

• Programming experience in Go or Python.

• Familiarity with software engineering practices for developing maintainable systems.

• Experience working with large datasets and building data pipelines for feature generation, model training, or inference.

• Proficiency in supervised and unsupervised machine learning techniques, including classification, clustering, similarity scoring, and anomaly detection.

• Ability to evaluate models using sound statistical methods.

• Understanding of model evaluation tradeoffs involving precision, recall, accuracy, and confidence.

• Ability to write clear, understandable, testable, and maintainable code.

• Strong communication skills, including the ability to explain technical concepts, model behavior, and tradeoffs to engineers, researchers, and product managers.

Work Arrangement

• Remote.

• Location: United States.