Staff Machine Learning Engineer

Job DescriptionMachine Learning EngineerCompany: Material SecurityLocation: San Francisco, CA (Remote available)Network SecurityEmployment Type: Full-timeCompensation: $225,000 - $255,000 (Projected Annual Range)Job OverviewMaterial Security is seeking a Machine Learning Engineer to join an experienced team dedicated to protecting user privacy. You will build, deploy, and maintain high-quality models designed to detect security-relevant data and behaviors, including phishing emails, sensitive data in emails/drives, fraud, and lateral account takeovers.Discover moreComputer SecurityText & Instant MessagingProgrammingKey ResponsibilitiesDesign, build, train, and deploy machine learning models to detect sensitive data and malicious threats (e.g., phishing emails).Write production-level code to create working ML pipelines and participate in code reviews to ensure quality.Architect scalable, reliable, and maintainable ML pipelines that integrate with existing backend systems.Explore recent advancements in Generative AI and LLMs to enhance detection capabilities.Collaborate with cross-functional teams (ML engineers, product managers, designers, data scientists) to align initiatives with business goals.Stay ahead of the curve by researching new algorithms, technologies, and frameworks.Contribute to engineering culture through active participation and mentorship.Required Qualifications (Must Haves)Education: B.S., M.S., or Ph.D. in Computer Science or a related technical field, or equivalent relevant work experience.Experience: 8+ years of experience in machine learning, data science, or related fields (or 6+ years with a Ph.D.). Minimum 3 years in a senior or staff engineering role.Expertise: Deep understanding of supervised/unsupervised learning techniques and Large Language Models (LLMs).Data Pipelines: Strong experience writing efficient and effective data pipelines.End-to-End Workflow: Proven practical knowledge of building end-to-end ML workflows, taking models from conception through deployment and maintenance.Tools: Experience with machine learning libraries (e.g., scikit-learn, Pandas).Preferred Qualifications (Nice To Have)Experience developing APIs (e.g., FastAPI).Experience tracking text embedding modeling.Strong knowledge of cloud platforms (AWS, GCP) and containerization tools (Docker, Kubernetes).