Key skills
AIMLNLPLLMLarge Language ModelsLeadershipA/B TestingCommunication
About this role
Role Overview
- Design, build, and operate the systems that serve ML models within the messaging stack, with a focus on latency, reliability, and scalability
- Write and review technical designs that solve large, open-ended problems at the intersection of ML and product engineering without clearly-known solutions
- Partner with ML, data science, and product teams to identify high-value opportunities, establish evaluation criteria, and close the gap between offline model performance and production impact
- Collaborate with other engineers and cross-functional partners across Messaging, Trust & Safety, Localization, and Platform organizations to align on long-term technical solutions
- Mentor, guide, advocate, and support the career growth of individual contributors
- Establish engineering standards for ML integration across the messaging surface, including feature flagging, A/B testing, observability, and graceful degradation
Requirements
- 9+ years of relevant engineering hands-on work experience
- Bachelors, Masters, or PhD in CS or related field
- Demonstrated experience building and shipping ML-powered product features in production environments, including model serving, feature pipelines, online/offline evaluation, and monitoring
- Exceptional architecture abilities and experience with architectural patterns of large, high-scale applications
- Familiarity with NLP/NLU techniques and large language models, particularly as applied to messaging, conversational AI, or content understanding
- Shipped several large-scale projects with multiple dependencies across teams, specifically at the intersection of ML infrastructure and product engineering
- Technical leadership and strong communication skills with the ability to translate between ML research, product goals, and engineering execution
- Experience operating distributed, real-time systems at scale with high reliability requirements
- Experience with real-time messaging systems or event-driven architectures
- Familiarity with ML infrastructure at scale (e.g., feature stores, model registries, online inference platforms)
- Prior work on trust & safety, content moderation, or internationalization in a messaging context
- Experience with LLM-based product features, including prompt engineering, retrieval-augmented generation, or fine-tuning.
Benefits
- This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.