Job description

Why 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. What We're Building 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. What We're Looking For We’re looking for a machine learning engineer to help build and scale the models and systems that power Doppel’s detection systems. Check out our blog post doppel.com on how we set up our ML platform. 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 What We Offer 🚀 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 JOIN DOPPEL Doppel is the first platform built to dismantle digital deception at scale. We scan over 150 billion entities daily and deploy continuously adaptive AI SOC agents, paired with expert human analysts, to uncover and disrupt the infrastructure behind phishing, impersonation, and online fraud before attacks can spread. Our Threat Grid turns every customer signal into shared intelligence, making each disruption smarter, faster, and more effective. We’re not just another cybersecurity company. We’re defining the future of social engineering defense, where trust is protected, and deception becomes unprofitable. Backed by top-tier investors and trusted by some of the world’s most recognized brands, Doppel is growing fast. If you’re driven to solve real-world problems with bold technology, we’d love to meet you.

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Why Join Doppel
What We're Building
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.
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
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

Nice-to-haves

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Keywords to mirror

aimachine learningml

Platform: ashby
Effort: quick

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