Machine Learning Engineer

Material Security is at the forefront of cybersecurity, specifically focusing on protecting users' privacy and digital assets. As a Machine Learning Engineer, you'll join an elite team of experienced engineers working on cutting-edge security solutions to protect users from various threats including breaches, targeted phishing, fraud, and lateral account takeover.

The role combines deep technical expertise in machine learning with practical security applications. You'll be responsible for the entire ML lifecycle - from design and development to deployment and maintenance of sophisticated models that detect security-relevant data and behavior. This includes building systems to identify phishing emails and sensitive data in emails and drives.

The position requires a strong background in machine learning, with at least 8 years of experience (or 6+ years with a Ph.D.) and 3 years in a senior or staff engineering role. You'll need deep expertise in supervised/unsupervised learning techniques, LLMs, and practical experience building end-to-end ML workflows. The role offers competitive compensation ranging from $200,000 to $240,000.

As a remote-first company with an office in San Francisco, Material Security offers flexibility in work location while maintaining a collaborative environment. You'll work closely with cross-functional teams including ML engineers, product managers, designers, and data scientists, contributing to both technical excellence and engineering culture through mentorship and knowledge sharing.

This is an excellent opportunity for an experienced ML engineer who wants to make a significant impact in the cybersecurity space, working with state-of-the-art technologies and tackling challenging problems that directly affect user privacy and security. The role offers the chance to work on meaningful projects while being part of a world-class engineering team.