Join Doppel

Doppel is building the future of social engineering defense. Our AI-native platform uses agentic AI to protect executives, employees, customers, and brands from phishing, impersonation, fraud, and other AI-powered threats across digital channels. We help some of the world's most recognized brands detect and dismantle attacker infrastructure while strengthening employee resilience through threat-informed training and simulation. By unifying Digital Risk Protection, Human Risk Management and Email Security, Doppel connects threats into a real-time intelligence graph to power faster disruption, smarter defense, and modern security awareness at scale.

Backed by leading investors including Andreessen Horowitz and Bessemer Venture Partners, and trusted by leading enterprises, Doppel is a rapidly growing Series C startup building the future of social engineering defense. Our team combines deep cybersecurity expertise, operational rigor, and startup velocity to solve some of the internet's most urgent trust and safety challenges.

We're building the AI-native social engineering defense platform.

This means we're designing scalable systems that monitor billions of domains, social media accounts, apps, dark web forums, etc., and leverage AI agents to identify and neutralize digital threats.

We're looking for a machine learning engineer to help build and scale the models and systems that power Doppel's detection systems. As an MLE at Doppel, you will

• Design, train, and deploy models for both batch and real-time inference that identify malicious or infringing content across diverse data sources.
• Partner closely with the Detection and Infrastructure teams to ensure our ML systems scale with the volume of web data we ingest
• Work on problems that range from NLP and embeddings to similarity search, classification, and anomaly detection
• Collaborate directly with customers and internal stakeholders to translate real-world threats into production ML systems

You may be a fit if you:

• Have experience building and deploying ML systems in production environments
• Are comfortable working with large-scale datasets and distributed data processing frameworks
• Understand the trade-offs between research-quality models and production-ready systems
• Are excited about solving real-world problems where the adversary is constantly evolving

A mission-driven culture with low ego, high ownership, deep customer obsession, and exceptional talent density

Free lunch and dinner in the office

Flexible PTO

Quarterly team offsites