Level

Doppel

Machine Learning Engineer, Detection (TOR)

Doppel is hiring a Machine Learning Engineer, Detection (TOR) in Toronto, Canada. It pays CAD 183k-CAD 429k a year and Level rates it ; you can apply on Doppel.

Level's read on this job

  • Builds AI ●●●● is how Level rates this machine learning engineer role: the job is building AI systems.
  • Tools and skills the posting cites include Python, PyTorch and AI agents.
  • Level classifies it as a mid-level role.
  • Level lists 23 other open roles at Doppel.

AI in this role

Design, train, and deploy machine learning models and systems that power AI-native social engineering detection systems.

pythonpytorch
ai-agentsnlpmachine-learninganomaly-detectionclassification

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.

How we rate this

Machine Learning Engineer, Detection (TOR) at Doppel rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI agentsNLPMachine learningAnomaly DetectionClassificationPythonPyTorch

Also common in machine learning engineer jobs at this level

Pay

Typical machine learning engineer job
$199k

Use the typical machine learning engineer figure as a reference when pay comes up. See machine learning engineer jobs

Questions you could be asked

  1. How do you decide when an AI agent can act on its own versus asking for approval first?
  2. What NLP problem have you worked on, and how did you measure whether it actually worked?
  3. Tell me about a project where machine learning was part of your work. What did you do?
  4. Tell me about a project where anomaly detection was part of your work. What did you do?
  5. Tell me about a project where classification was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: AI agents, NLP, Machine learning, Anomaly Detection, and Classification. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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