Senior Machine Learning Engineer

Company Background

Censys’ mission is to be the one place to understand everything on the internet. Frustrated by the lack of trustworthy Internet intelligence, we set out to create the industry’s most comprehensive, accurate, and up-to-date map of the Internet. Today, Censys delivers real-time Internet intelligence and actionable threat insights to global governments, over 50% of the Fortune 500, and leading threat intelligence providers worldwide.

Censys is building the most credible, robust map of the Internet through IP scanning, DNS lookups, web crawling, and the ingestion of millions of certificates. Censys was founded by security researchers who are passionate about developing technology that provides anyone the power to fully understand their digital risk and exposure. Individuals and enterprises–and anyone in between–can harness this power to discover new information and insight as the Internet, IoT and Cloud evolve. We are a true security startup with midwestern roots and we believe that a map of the Internet, or a map of your organization’s assets can quickly help guide organizations to the answers they need to protect themselves from vulnerability and risk.

Location: This position is remote within the United States.

Role Summary:

We’re looking to hire a Senior Machine Learning Engineer to build models and data-driven systems that help classify, label, and enrich vast amounts of Internet data, providing direct value to customers and other parts of our organization. Censys operates distributed infrastructure for Internet-wide scanning and you will help us continue our mission to transform raw Internet telemetry into high-quality datasets, classifications, and insights about the Internet at large.

At Censys, we believe in working iteratively, while keeping the big picture in mind. We’re expanding our data platform to enable future products and features that make the Internet more explainable by adding richer context and showing complex relationships. We’re looking for someone who is curious, collaborative, and excited to grow while contributing to our mission.

What You’ll Do:

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 ML 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 we’re building the right models, datasets, and feedback loops to improve coverage and quality.

Leverage your experience in machine learning, data science, and software engineering to build various parts of the system, including components like: feature pipelines, training datasets, model evaluation frameworks, confidence scoring systems, and services that run in the cloud or on-prem.

Skills You Have:

5+ years of experience in data science, machine learning engineering, or software engineering with applied ML responsibilities.

Experience building and deploying machine learning or statistical models in production environments.

Experience programming in Go/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, such as 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 towards maintainability

Possess strong communication skills and can explain technical concepts, model behavior, and tradeoffs to engineers, researchers, and product managers.

Things that make you stand out:

Experience building classification, enrichment, or labeling systems for messy or partially labeled data.

Experience deploying models in containerized environments, like Kubernetes.