Senior Staff Machine Learning Engineer, Ml Understanding

Details

Reddit is looking for a Senior Staff Machine Learning Engineer to lead its next-generation user understanding initiative: building a unified, high-fidelity representation of each user that powers personalization across the platform.

This role requires deep expertise in mainstream ML user modeling approaches (e.g., large-scale embeddings, user interest modeling, affinities, behavioral signals) and the ability to reimagine these systems in the GenAI era—leveraging LLMs and foundation models to unlock step-change improvements in fidelity, adaptability, and expressiveness.

You will set the technical direction for the user understanding space, leading the design and implementation of Reddit’s core user representation layer—spanning embeddings, interest modeling, and key user attributes. You’ll ensure this foundation is scalable, reliable, and widely adopted across Feeds, Search, Notifications, and Ads, partnering closely with product, infrastructure, and downstream ML teams to drive measurable impact.

Responsibilities

• Design User Understanding Strategy: Define a unified user understanding framework and strategy—how users are represented (embeddings, tags, attributes, LLM-based user profile), how they are computed, stored, and exposed. Provide thought leadership by setting a long-term technical vision and advancing the state-of-the-art in user understanding and user modeling.
• Build Foundational User Models: Lead design and implementation of advanced user models (e.g., large-scale user representation learning such as sequence-based, multi-interest, multi-task) that share representations across surfaces to improve personalization across key Reddit products (Feeds, Notification, Search, and Ads), balancing latency, cost, and performance.
• Reimagine user understanding with LLM/Gen-AI: Evolve user modeling beyond traditional representations by leveraging LLMs (e.g., dynamic user profiles, intent inference, semantic reasoning over user behavior). Explore how LLMs can augment or unify embeddings, attributes, and taxonomies for more adaptive, interpretable, and context-aware personalization.
• Ship Large Scale User Understanding as a System: Partner with platform teams to design and build core components for large-scale learning and serving, including storage/retrieval for embeddings, feature pipelines, and APIs. Collaborate with ML/Ranking infrastructure to ensure low-latency serving, high availability, and integration with MLOps systems.
• Drive Cross-Team Integration & Impact: Partner with Feeds, Notification, Search, and Ads teams to drive experimentation and adoption of new user understanding models with product teams, ensuring measurable end-to-end impact on key metrics.
• Set Technical Bar & Mentor: Mentor senior to staff engineers, lead design reviews, steward technical decisions across the user understanding domain, and champion engineering processes and best practices.

Requirements

• At least 10 years experience building and scaling production-grade ML systems, particularly in user modeling, large-scale representation learning, or recommender systems.
• Track record of driving ambiguous, high-impact initiatives from concept to production and shaping technical direction and execution.
• Product- and impact-oriented mindset: care about how work moves real metrics (e.g., engagement, retention, revenue), not just model quality.
• Strong fundamentals in mainstream user understanding ML approaches (e.g., representation learning, behavioral modeling, user clustering) and understanding their trade-offs in real-world systems.
• Excited about the GenAI shift and experience (or strong intuition) applying LLMs or foundation models to evolve existing systems.
• Think in systems: consider data, training, evaluation, serving, and adoption cohesively and design with end-to-end impact.
• Influence beyond immediate team: partner effectively with product, infra, and other ML teams to drive alignment.
• Comfortable raising the technical bar: mentor senior engineers, lead design reviews, and establish best practices for reliable, scalable ML systems.
• Comfortable navigating trade-offs across quality, latency, cost, and safety in large-scale, user-facing systems.

Benefits

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave