Senior Machine Learning Engineer

Role overview

A senior engineering role focused on building and deploying applied machine learning systems that classify, cluster, label, and enrich large-scale Internet telemetry data. The work sits at the intersection of distributed data infrastructure and production ML engineering, turning raw observations into structured insight consumed by internal systems and customer-facing products.

Responsibilities

- Design, build, and improve ML models and data-driven systems that classify, cluster, label, and enrich Internet-observed assets and services - Own end-to-end applied ML workflows that transform raw telemetry into usable context for products and platforms - Partner with engineering, research, security, and product teams to define datasets, models, and feedback loops that improve coverage and quality - Develop system components including feature pipelines, training datasets, evaluation frameworks, confidence scoring, and cloud or on-prem serving services - Evaluate models using sound statistics, balancing precision, recall, accuracy, and confidence tradeoffs - Write understandable, testable, maintainable code and contribute to engineering best practices

Requirements

- 5+ years of experience in data science, ML engineering, or software engineering with applied ML responsibilities - Hands-on experience building and deploying ML or statistical models in production environments - Proficiency in Go or Python and familiarity with software engineering best practices for maintainable systems - Experience working with large datasets and building pipelines for feature generation, training, or inference - Knowledge of supervised and unsupervised learning techniques such as classification, clustering, similarity scoring, and anomaly detection - Strong communication skills with the ability to explain technical concepts and model behavior 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 such as Kubernetes - Familiarity with at least one major cloud provider, such as AWS, Azure, or GCP - Experience with feature stores, model serving, and MLOps workflows