Senior Machine Learning Engineer

Censys

2d•$150,000 - $203,000•Remote

About The Position

Censys is seeking a Senior Machine Learning Engineer to develop models and data-driven systems for classifying, labeling, and enriching Internet data. This role is crucial for transforming raw Internet telemetry into high-quality datasets, classifications, and insights. The engineer will contribute to Censys' mission of making the Internet more explainable by adding context and illustrating complex relationships. The ideal candidate is curious, collaborative, and eager to grow within the company.

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.
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.
Strong communication skills and can explain technical concepts, model behavior, and tradeoffs to engineers, researchers, and product managers.
Open to using AI to amplify their skills and strengthen their work - demonstrating curiosity, a willingness to learn, and sound judgment in applying AI responsibly to improve efficiency and impact.

Nice To Haves

Experience building classification, enrichment, or labeling systems for messy or partially labeled data.
Experience deploying models in containerized environments, like Kubernetes.
Experience with at least one cloud provider, like: 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.

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