Censys
Internet intelligence
SeniorMachineLearningEngineer
“Senior Machine Learning Engineer at Censys. Skills: machine learning models, data-driven systems, classification, clustering, labeling, enrichment, feature pipelines, training datasets, model evaluation frameworks, confidence scoring systems, cloud, on-prem, statistical models, supervised learning, unsupervised learning, similarity scoring, anomaly detection, Kubernetes, AWS, Azure, GCP, feature stores, model serving, MLops workflows, experiment tracking. Build and improve machine learning model”
What You'll Achieve.
providing direct value to customers and other parts of our organization; transform raw Internet telemetry into high-quality datasets, classifications, and insights about the Internet at large; enable future products and features that make the Internet more explainable by adding richer context and showing complex relationships; improve coverage and quality
Industry & Context.
keep their cameras on during video interviews, in-person onboarding at HQ in Ann Arbor
What They're Looking For.
Must 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, 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, Communication skills and can explain technical concepts, model behavior, and tradeoffs to engineers, researchers, and product managers
Nice to Have
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
What You'll Do.
Build and improve machine learning models and data-driven systems that classify
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
Leverage your experience in machine learning
and software engineering to build various parts of the system
including components like: feature pipelines
model evaluation frameworks
confidence scoring systems
and services that run in the cloud or on-prem
How You'll Work.
Team & Collaboration
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
Communication Scope
explain technical concepts, model behavior, and tradeoffs to engineers, researchers, and product managers
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