Machine Learning Manager, Feed Relevance
Lead a team of ML engineers building large-scale retrieval systems for Reddit's personalized feeds. Requires 2+ years managing ML/recommender teams plus deep expertise in production recommender systems, embeddings, and transformers.
About the job
Responsibilities
- Define the technical vision and long-term roadmap for Feed Retrieval, aligning large-scale recommender-system investments with Reddit’s product, ecosystem, and business objectives.
- Translate broad Feed Relevance goals into a focused team roadmap, making clear prioritization tradeoffs across model quality, inventory expansion, experimentation velocity, infrastructure cost, and operational reliability.
- Coach and support the development of your team, constantly seeking opportunities to grow their skills and impact.
- Oversee the design, development, and optimization of retrieval systems that source relevant, diverse, fresh, and high-quality candidates for personalized feed experiences.
- Establish strong measurement, experimentation, and debugging practices so the team can understand retrieval quality, candidate coverage, source incrementality, and downstream impact.
- Collaborate with ML platform, infrastructure, ranking, safety, and product teams to build scalable, low-latency retrieval systems that can support the next generation of AI-powered recommendations.
- Maintain high standards for system performance, reliability, latency, cost efficiency, and responsible recommendation practices.
- Work with cross-functional partners from across the company to identify key areas of opportunity, set expectations, and communicate your team’s work.
- Partner with our recruiting team to attract, interview, and hire diverse and talented machine learning engineers, growing a world-class team.
Qualifications
- 2+ years of experience building and managing high-performing ML or recommender-systems teams.
- Hands-on experience with large-scale production ML systems, ideally including recommender systems, retrieval models, embedding-based systems, sequence models, transformer-based architectures, or LLM-powered recommendation applications.
- Strong understanding of recommender systems, especially candidate retrieval, embedding/indexing systems, ranking handoffs, feed personalization, exploration, content quality, and measurement strategies.
- Ability to develop and communicate a clear technical strategy across ambiguous problem spaces, balancing user relevance, ecosystem health, system scalability, and business impact.
- Passion for developing scalable, well-designed, and responsible AI solutions that drive business value.
- 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.
Nice-to-Haves
- Experience with recommender systems, retrieval models, embedding-based systems, sequence models, transformer-based architectures, or LLM-powered recommendation applications.
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
Skills
Machine Learning, Recommender Systems, Retrieval Models, Embeddings, Transformers, LLMs, Python, Experimentation, Measurement, Roadmapping
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