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.

• →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.

• 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.

This job posting may span more than one career level.

In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/.

To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below.