Staff Machine Learning Engineer

🕒 May 13

🏄 California – Remote

🏄 California – Remote

💵 $225k - $255k / year

⏰ Full Time

🔴 Lead

🤖 Machine Learning Engineer

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Material Security

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2017

🔒 Cybersecurity

🔐 Security

Cybersecurity • Software • Security

Material Security is a cybersecurity company focused on multi-layered detection and response solutions specifically for email. It offers products that provide phishing protection, identity protection, and data protection for emails and Google Drive. The company's solutions are designed to detect, contain, and prevent email account takeovers, block unauthorized access, and govern email data. Material Security utilizes advanced technologies including AI and behavioral analysis to enhance security across email platforms like Microsoft 365 and Google Workspace.

📋 Description

• 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.

🎯 Requirements

• 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 own the entire process of model development from conception through deployment, to maintenance.. • Experience with machine learning libraries (e.g., scikit, Pandas)

🏖️ Benefits

• Health insurance • Retirement plans • Paid time off • Flexible work arrangements • Professional development