About the role

Role Overview

Join Material Security as a Staff Machine Learning Engineer (full remote). You’ll build, deploy, and maintain high-quality models that detect security-relevant data and behavior, and you’ll architect scalable ML pipelines aligned with business goals.

Responsibilities

Design, build, train, and deploy machine learning models to detect sensitive data and malicious threats.
Write production-quality code to turn ML models into working, maintainable pipelines and participate in code reviews.
Architect scalable, reliable, maintainable ML pipelines and integrate them with existing backend systems.
Explore advancements in generative AI/LLMs and collaborate across teams to align ML initiatives with business objectives.

Requirements

8+ years of experience (or Ph.D. with 6+ years) in machine learning, data science, or related fields, including at least 3 years in a senior/staff engineering role.
Experience with ML libraries such as scikit-learn and Pandas.
Strong ability to own the full ML lifecycle: conception → deployment → maintenance.
Strong experience building efficient end-to-end ML workflows and data pipelines.
Deep understanding of supervised/unsupervised learning and LLMs.
Experience developing APIs using FastAPI.
Experience with text embedding modeling tracking.
Strong knowledge of cloud platforms (AWS/GCP) and containerization tools (Docker, Kubernetes).

Nice-to-haves

Experience with LLM-focused production systems and embedding/model monitoring at scale.

About Material Security

Material Security is a security-focused company helping protect users’ privacy by detecting sensitive data and malicious behavior. The role involves building and deploying production-grade machine learning models and ML pipelines.