Director of Machine Learning, Safety

Discord; San Francisco (CA); On-site; USD 150,000 - 240,000; Full time

Job summary

A leading company in the tech industry is seeking a visionary Director of Machine Learning for their Trust & Safety team. The role demands extensive experience in ML and data science to innovate and implement scalable solutions that enhance user safety and effectively moderate content, ensuring a secure environment for users. Candidates should have a proven track record of cross-functional collaboration, leadership, and successful project outcomes in the ML landscape.

Qualifications

• Over 8 years of experience in machine learning and data science.
• 5+ years directing high-impact teams.

Responsibilities

• Develop and deploy scalable ML solutions for user safety and fraud prevention.
• Drive cross-functional safety initiatives with various teams.

Skills

Leadership of multidisciplinary ML teams

Technical strategy for scalable ML infrastructure

End-to-end ownership of machine learning pipelines

Collaboration with Policy, Legal, and Risk

Innovation in real-time harm detection

ML insights into measurable user protection outcomes

Job description

Director of Machine Learning – Trust & Safety

Location: Remote | United States

Visionary ML leader with over 8 years of experience in machine learning and data science, including 5+ years directing high-impact teams. Proven success in developing and deploying scalable ML solutions that enhance user safety, prevent fraud, and support content moderation across consumer tech platforms. Adept at driving cross-functional safety initiatives in collaboration with Policy, Legal, and Trust & Safety teams to safeguard millions of users.

Core Competencies:

• Leadership of multidisciplinary ML teams in Trust & Safety, Fraud Prevention, and Content Moderation
• Technical strategy for scalable ML infrastructure and SOTA model development
• End-to-end ownership of machine learning pipelines, from creative data sourcing to production deployment
• Collaboration with Policy, Legal, and Risk to ensure ethical, impactful ML applications
• Innovation in real-time harm detection, proactive safety systems, and reactive workflows
• Proven ability to translate ML insights into measurable user protection outcomes

Key Achievements:

• Scaled and led a top-performing ML Safety team, significantly reducing harmful content through predictive modeling and automated moderation
• Launched ML-driven fraud prevention systems that improved detection rates while reducing false positives
• Championed technical decisions for an evolving ML stack, ensuring platform scalability and long-term sustainability
• Mentored senior ML scientists and engineers, fostering a high-performance, impact-driven culture

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