Staff Machine Learning Engineer
Material Security
Machine Learning Engineer
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).
Responsibilities
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.
What We're Looking For
Must Haves
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)
Nice to have
Experience in API development on top of a fast API
Experience tracking text embedding modeling
Strong knowledge of cloud platforms (e.g., AWS, GCP) and containerization tools (e.g., Docker, Kubernetes).
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