Machine Learning Manager, Feed Ecosystems

Reddit is looking for an experienced Engineering Manager to lead our Feed Ecosystems team. In this role, you’ll lead a high-impact team of Machine Learning Engineers focused on building recommendation systems that support sustainable growth across users, posts, and communities. Your team will improve relevance for new, low-signal, and logged-out users; help new posts and communities find the right audiences; and develop ML systems that balance personalization, discovery, and ecosystem quality across Reddit’s 100,000+ active communities.

If applying ML / AI in production to improve Reddit Relevance and strengthen Reddit’s community ecosystem excites you, then you’ve found the right place.

Responsibilities:

• Define Technical Vision & Strategy: Define the technical vision and long-term roadmap for Feed Ecosystems, aligning recommender-system investments with Reddit’s goals around user growth, contribution, community health, and high-quality discovery.
• Team Leadership & Development: Coach and support the development of your team, constantly seeking opportunities to grow their skills and impact.
• Cross-Functional Partnership: Work closely with product, design, data science, safety, community, ads, and platform partners to identify opportunities, set expectations, and communicate your team’s work.
• Technical Execution & Delivery: Oversee the design, development, and optimization of ML systems that improve cold-start relevance, new post and community distribution, community discovery, and feed quality.
• Quality & Ecosystem Measurement: Help define and operationalize signals for subjective and objective quality, ensuring Feed systems optimize not only for engagement, but also for user value, community health, contribution, and long-term ecosystem outcomes.
• Platform & Infrastructure Collaboration: Collaborate with platform and infrastructure teams to build scalable AI-powered systems that support discovery, personalization, and healthy content distribution across Reddit.
• Operational Excellence: Maintain high standards for system performance, reliability, efficiency, and responsible AI practices in alignment with user needs and ecosystem health.
• Recruiting & Growth: Partner with our recruiting team to attract, interview, and hire diverse and talented machine learning engineers, growing a world-class team.

Qualifications:

• Experience Leading ML Teams: 2+ years of experience building and managing high-performing ML or recommender-systems teams.
• Deep ML Expertise: Hands-on experience with large-scale production ML systems, ideally including recommender systems, personalization, cold-start modeling, content understanding, or LLM-powered recommendation applications.
• Technical Domain Knowledge: Strong understanding of recommender systems, including candidate retrieval, ranking, value modeling, ecosystem dynamics, and measurement strategies.
• Strategic Thinking: Ability to develop and communicate a clear, compelling technical strategy across ambiguous problem spaces, balancing user relevance, community growth, content quality, safety, and business impact.
• Impact-Driven Mindset: Passion for developing scalable, well-designed, and responsible AI systems that improve user value while supporting a healthy and sustainable content ecosystem.
• Exceptional Communication & Collaboration: Strong interpersonal skills and a collaborative mindset, with the ability to effectively communicate complex technical topics to diverse audiences and build strong relationships with cross-functional partners.

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

Originally posted on Himalayas