Jobs Companies Doppel Machine Learning Engineer, Detection (TOR)

Über diese Machine Learning Engineer, Detection (TOR) Stelle bei Doppel

Doppel · Toronto

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 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.

Bereit, sich bei Doppel zu bewerben?
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Wie sich dieses Gehalt für ML Engineer vergleicht

Diese Stelle zahlt $220,570/yrim Einklang mit der üblichen Spanne für ML Engineer Stellen.

$126,733 dem Median $195,000 $295,600

Übliche Spanne $143,485–$242,500/yr, aus 29 vergleichbaren ML Engineer Anzeigen auf JobsRadar (Vergütung auf USD hochgerechnet). Gehaltseinblicke für ML Engineer ansehen →

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