Description:

As a Machine Learning Engineer at Material Security, you will be part of a team of experienced engineers dedicated to protecting users and their privacy from threats such as breaches, phishing, fraud, and account takeovers.
Your mission includes building, deploying, and maintaining high-quality models that detect security-relevant data and behavior, including phishing emails and sensitive data in emails and drives.
Responsibilities include designing, building, training, and deploying machine learning models to detect sensitive data and malicious threats.
You will write production-level code to convert ML models into working pipelines and participate in code reviews to ensure code quality and knowledge distribution.
The role involves architecting scalable, reliable, and maintainable machine learning pipelines that integrate seamlessly with existing backend systems.
You will collaborate closely with machine learning engineers, product managers, designers, data scientists, and software engineers to align machine learning initiatives with business goals.
Staying ahead of the curve by exploring new algorithms, technologies, and frameworks to enhance detection models is also part of your responsibilities.
You are expected to contribute to a great engineering culture through active participation and mentorship.

Requirements:

A B.S., M.S., or Ph.D. in Computer Science or a related technical field, or relevant work experience is required.
You must have 8+ years of experience in machine learning, data science, or related fields, with at least 3 years in a senior or staff engineering role (or a Ph.D. with 6+ years of experience).
A deep understanding of supervised and unsupervised learning techniques and large language models (LLMs) is essential.
Strong experience in writing efficient and effective data pipelines is required.
Practical knowledge of building efficient end-to-end ML workflows and a strong drive to own the entire process of model development from conception through deployment to maintenance is necessary.
Experience with machine learning libraries such as scikit-learn and Pandas is required.
Nice to have: Experience in API development on top of a fast API, experience tracking text embedding modeling, and strong knowledge of cloud platforms (e.g., AWS, GCP) and containerization tools (e.g., Docker, Kubernetes).

Material Security offers a remote-first workplace with an office located in San Francisco, California.
Compensation for this position is projected to range from $200,000 to $240,000, determined by various factors including the individual’s knowledge, skills, competencies, and experience.