Machine Learning Engineer, Detection (SF/NY)

Doppel seeks a Machine Learning Engineer to build and scale the models and systems behind its digital-threat detection platform. The platform monitors domains, social media accounts, apps and dark web forums to identify threats including phishing, impersonation and fraud. The role is based in San Francisco or New York with a hybrid working arrangement.

Responsibilities

• Design, train and deploy models for batch and real-time inference to identify malicious or infringing content across diverse data sources.
• Partner with Detection and Infrastructure teams to scale ML systems alongside the volume of web data ingested.
• Address detection problems involving NLP, embeddings, similarity search, classification and anomaly detection.
• Work with customers and internal stakeholders to turn evolving real-world threats into production ML systems.

Candidate profile:

• Experience building and deploying ML systems in production environments, working with large-scale datasets and using distributed data processing frameworks.
• Understanding of the trade-offs between research-quality models and production-ready systems, and interest in adversarial problems that continually evolve.
• No specific years of experience or education requirement is stated.

The full-time position lists compensation of $150K–$400K per year and offers equity. Benefits mentioned include free lunch and dinner in the office, flexible PTO and quarterly team offsites.