Doppel
Engineering
MachineLearningEngineer,Detection(TOR)
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Machine Learning Engineer, Detection (TOR) at Doppel. Skills: Machine Learning, model deployment, large-scale datasets, distributed data processing, NLP, embeddings, similarity search, classification, anomaly detection. 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”
What You'll Achieve.
make the internet a safer place by outsmarting the world’s fastest-evolving digital threats; identify and neutralize digital threats; ensure our ML systems scale with the volume of web data we ingest; translate real-world threats into production ML systems; protect trust, and make deception unprofitable
Industry & Context.
outsmart one of the great threats AI presents: mass-manufactured social engineering; outsmarting the world’s fastest-evolving digital threats; solve real-world problems with bold technology; translate real-world threats into production ML systems; solving real-world problems where the adversary is constantly evolving; dismantle digital deception at scale; uncover and disrupt the infrastructure behind phishing, impersonation, and online fraud
What They're Looking For.
Must Have
experience building and deploying ML systems in production environments, comfortable working with large-scale datasets and distributed data processing frameworks, understand the trade-offs between research-quality models and production-ready systems
What You'll Do.
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
and anomaly detection
Collaborate directly with customers and internal stakeholders to translate real-world threats into production ML systems
How You'll Work.
Team & Collaboration
Partner closely with the Detection and Infrastructure teams; Collaborate directly with customers and internal stakeholders
Full Job Description
Why Join Doppel Doppel is built to outsmart one of the great threats AI presents: mass-manufactured social engineering. Countless scams, deepfakes, and other social engineering attacks are surging across every digital channel: websites, social media, ads, encrypted messaging apps, mobile, and more. Our mission is simple but bold: make the internet a safer place by outsmarting the world’s fastest-evolving digital threats. Backed by a16z and Bessemer and trusted by some of the world’s most recognized brands (OpenAI, United Airlines, Coinbase, etc.), Doppel is growing fast. If you’re driven to solve real-world problems with bold technology, we’d love to meet you. 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 https://www.doppel.com/blog/battling-scams-at-scale-inside-doppels-high-throughput-ml-platform?utm_source=chatgpt.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 distribute
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