Staff Machine Learning Engineer, Material Security

• B.S., M.S. or Ph.D. in Computer Science or related technical field or relevant work experience
• 8+ years (or Ph.D. with 6+ years) of experience in machine learning, data science, or related fields, with at least 3 years in a senior or staff engineering role
• Deep understanding of supervised/unsupervised learning techniques and LLMs
• Strong experience writing efficient and effective data pipelines
• Practical knowledge of how to build efficient end-to-end ML workflows and a strong drive to won the entire process of model development from conception through deployment, to maintenance.
• Experience with machine learning libraries (e.g., scikit, Pandas)

Desirable

• As a Machine Learning Engineer at Material Security, you'll be part of a team of experienced, world-class engineers, working to protect our users and their privacy (e.g., inboxes from breaches, targeted phishing, fraud, and lateral account takeover)
• Your mission is to build, deploy, and maintain high quality models that detect security relevant data and behavior (phishing emails, sensitive data in email and drives)
• Design, build, train, and deploy machine learning models to detect sensitive data and malicious threats (phishing emails)
• Write production-level code to convert your ML models into working pipelines and participate in code reviews to ensure code quality and distribute knowledge
• Architect scalable, reliable, and maintainable machine learning pipelines, integrating seamlessly with existing backend systems
• Explore recent advancements in generative AI and LLMs as potential additions to our detection capabilities
• Work closely with machine learning engineers, product managers, designers, data scientists, and software engineers to align machine learning initiatives with business goals
• Stay ahead of the curve by exploring new algorithms, technologies, and frameworks to enhance our detection models
• Contribute to great engineering culture through active participation and mentorship