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Salary: USD 151,000 - 206,000 / annual
Censys is building the most comprehensive and accurate map of the Internet through IP scanning, DNS lookups, web crawling, and certificate ingestion. The company delivers real-time Internet intelligence and actionable threat insights to global governments, over 50% of the Fortune 500, and leading threat intelligence providers.
As a Senior Machine Learning Engineer, you will build and improve machine learning models and data-driven systems that classify, cluster, label, and enrich Internet-observed assets and services. You will own the design and development of applied ML workflows that transform raw Internet telemetry into usable context for internal systems and customer-facing products.
Key responsibilities include:
- Building and improving ML models and data-driven systems for classification, clustering, labeling, and enrichment of Internet assets
- Owning the design and development of applied ML workflows that turn raw telemetry into actionable context
- Partnering with engineering, research, security, and product teams to ensure models, datasets, and feedback loops improve coverage and quality
- Leveraging ML, data science, and software engineering expertise to build system components including feature pipelines, training datasets, model evaluation frameworks, confidence scoring systems, and cloud/on-prem services
The role is fully remote within the United States. Censys is headquartered in Ann Arbor, Michigan, and offers in-person onboarding at HQ.
REQUIREMENTS:
- 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
- Programming proficiency in Go/Python with familiarity with software engineering practices for maintainable systems
- Experience working with large datasets and building data pipelines for feature generation, training, or inference
- Proficiency with supervised and unsupervised learning techniques (classification, clustering, similarity scoring, anomaly detection)
- Ability to evaluate models using sound statistics and understand tradeoffs related to precision, recall, accuracy, and confidence
- Ability to write understandable, testable, maintainable code
- Strong communication skills to explain technical concepts, model behavior, and tradeoffs to engineers, researchers, and product managers
DESIRABLE QUALIFICATIONS:
- Experience building classification, enrichment, or labeling systems for messy or partially labeled data
- Experience deploying models in containerized environments like Kubernetes
- Experience with cloud providers (AWS, Azure, or GCP)
- Familiarity with feature stores, model serving, MLOps workflows, or experiment tracking tools
- Familiarity with security, Internet measurement, or network-derived datasets
