About the job
As a Senior/Principal Machine Learning Engineer for Safety AI Systems, you will define the future of proactive moderation, driving immense social impact through cutting-edge, innovative ML solutions. This role is a fusion of a Principal Engineer's technical authority and an Engineering Manager's leadership, focused on the most critical and ambiguous safety challenges.
Responsibilities
Define and Own the Technical Vision: Define and lead the multi-year technical vision, architectural strategy, and execution for machine learning solutions across Content and Communication Safety, ensuring these systems proactively and effectively detect and prevent high-severity, critical harms at massive scale.
Provide Senior/Principal-Level Architecture: Act as the highest technical authority for the Content Safety ML domain, guiding the architecture and long-term maintainability of foundational models, data pipelines, and real-time inference services.
Drive Cross-Org Alignment: Identify and champion the most ambiguous, high-leverage technical problems, driving alignment and securing investment for organization-wide ML infrastructure and platform development initiatives that benefit all of Trust & Safety.
Lead Innovation in Safety: Oversee the adoption and safe deployment of innovative technologies (e.g., advanced NLP, self-supervised learning, multimodal LLMs) to anticipate and mitigate novel abuse vectors, moving beyond reactive detection to proactive intervention.
Strategic Stakeholder Partnership: Collaborate with executive-level Product, Data Science, Policy, and Operations leaders to define and prioritize the strategic machine learning roadmap, influencing product strategy and demonstrating the impact of ML on user trust and safety outcomes.
Qualifications
Minimum
8+ years of experience designing, developing, and operating large-scale, high-impact machine learning systems in a production environment.
5+ years of experience in technical leadership, management, or mentorship roles, ideally having managed Engineering Managers or Senior/Principal-level individual contributors.
A proven track record of successfully setting the long-term technical direction for an entire ML domain or pillar, demonstrating the ability to take ambiguous problems from concept to scaled production impact.
Deep expertise in advanced ML architectures, including Large Language Models (LLMs), transfer learning, or other foundation model technologies, especially applied to text or multimodal data.
Expertise in architecting scalable, real-time ML inference services and robust data pipelines operating at millions of requests per second.
Demonstrated success in leading and resolving high-stakes, cross-functional conflicts and technical disagreements, with an ability to build consensus among diverse stakeholders.
Exceptional product sense and strategic planning ability: able to translate platform safety requirements into an achievable, iterative technical roadmap.
Preferred
No preferred qualifications listed.