About the role

Role overview

As a Machine Learning Engineer (Detection, TOR), you will design, train, and deploy machine learning models to detect malicious content across multiple data sources. You’ll work with internal teams and customers to translate real-world threats into production ML systems.

Key missions

Design, train, and deploy ML models for batch and real-time inference to identify malicious content.
Collaborate with Detection and Infrastructure teams to ensure ML systems scale with the volume of web data.
Tackle ML problems across NLP, embeddings, similarity search, classification, and anomaly detection.

Requirements

Experience building and deploying ML systems in production.
Ability to balance research-quality models vs production-ready systems (trade-offs).
Comfort working with large-scale datasets and distributed data processing frameworks.
Motivation to solve real-world problems where the adversary evolves.

Nice-to-haves

Not explicitly stated in the posting.

About Doppel

Doppel is an AI-native social engineering defense platform focused on protecting against malicious content. The company builds machine-learning systems to detect and respond to evolving adversarial threats across multiple data sources.