Senior Product Manager, ML Signals

Reddit is a community of communities, built on shared interests and authentic conversations. As a Senior Product Manager for ML Signals, you will define and execute the team roadmap, collaborating with machine learning engineers and product managers to enhance content understanding signals across various modalities.

Responsibilities:

• Signal Strategy & Vision: Define the long-term roadmap for foundational "content understanding" signals, including topicality (interest-based tagging), content maturity, and quality scores used across the platform
• Platform Adoption: Act as the primary bridge between the MLE team and downstream consumers (e.g., Feed, Ads, Search, and Safety). Drive the integration of ML signals to improve personalization and brand safety
• Signal Governance: Establish standardized definitions and success metrics for content quality and utility, ensuring a consistent understanding of "high quality" across different product surfaces
• Data Strategy & Labeling: Lead the strategy for high-quality training data, including the use of "Human-in-the-Loop" (HITL) workflows and modern automated evaluation techniques (e.g., using Large Language Models to assist in data labeling)
• Performance Optimization: Partner with Data Science and MLEs to monitor signal health, coverage, and accuracy (Precision/Recall), and translate technical performance into business impact (e.g., retention, engagement, or revenue)
• Cross-Functional Leadership: Navigate complex tradeoffs between Policy, Engineering, and Product teams to ensure signals meet both user safety requirements and product growth goals

Requirements:

• 5+ years of Product Management experience, specifically focused on Machine Learning, NLP, or Platform products
• A proven history of shipping high-impact products that have delivered measurable business results
• Autonomy in roadmap management, with the ability to lead complex services from initial vision to execution and scaling
• Strategic vision, including the ability to define clear goals and success metrics that align with broader organizational objectives
• Deep understanding of the ML lifecycle, encompassing data collection, feature engineering, model evaluation, and deployment
• Technical proficiency in modern AI, including comfortable fluency in embeddings, classifiers, ranking systems, and LLM capabilities
• Direct experience with LLM-based evaluation frameworks (e.g., 'LLM-as-a-judge') and the design of scalable, automated data curation pipelines
• Analytical self-sufficiency, with the ability to independently gather and derive actionable insights from complex datasets
• The ability to synthesize technical complexity into clear product requirements and executive-level summaries
• Cross-functional influence, with a demonstrated ability to drive internal adoption of platform tools and integrate technical signals into diverse product roadmaps
• Effective technical partnership skills, maintaining the depth of knowledge required to collaborate closely with ML engineers and architects
• Professional background in Data Science or Engineering, providing a hands-on perspective on technical implementation
• Active engagement with the ML research community, including staying current with the latest white papers and industry breakthroughs