Machine Learning Manager, Feed Relevance – Retrieval

atReddit, Inc.Remote

United States Full-time Machine Learning EngineerJuniorMid-level$253.3k – $354.6k/year

Posted Jul 29

This is a fully remote position, open to applicants in United States.

📋 Description

• Define the technical vision and long-term strategy for Feed Retrieval, ensuring alignment between large-scale recommender-system investments and Reddit’s product, ecosystem, and business goals.

• Convert broad Feed Relevance objectives into a targeted team roadmap, clearly outlining prioritization trade-offs involving model quality, inventory expansion, experimentation speed, infrastructure costs, and operational reliability.

• Mentor and empower your team’s development, consistently looking for opportunities to enhance their skills and impact.

• Supervise the design, development, and optimization of retrieval systems that provide relevant, diverse, fresh, and high-quality candidates for personalized feed experiences.

• Implement robust measurement, experimentation, and debugging practices to help the team evaluate retrieval quality, candidate coverage, source incrementality, and downstream effects.

• Collaborate with ML platform, infrastructure, ranking, safety, and product teams to create scalable, low-latency retrieval systems that support the next generation of AI-driven recommendations.

• Uphold high standards for system performance, reliability, latency, cost-effectiveness, and responsible recommendation practices.

• Engage with cross-functional partners throughout the organization to identify significant opportunities, set expectations, and effectively communicate your team’s contributions.

• Work alongside our exceptional recruiting team to attract, interview, and hire diverse and talented machine learning engineers, building a world-class team.

⛳️ Requirements

• A minimum of 2 years of experience in building and managing high-performing ML or recommender-systems teams.

• Practical experience with large-scale production ML systems, particularly including recommender systems, retrieval models, embedding-based systems, sequence models, transformer-based architectures, or LLM-driven recommendation applications.

• In-depth understanding of recommender systems, particularly candidate retrieval, embedding/indexing systems, ranking handoffs, feed personalization, exploration, content quality, and measurement strategies.

• Capability to formulate and convey a clear technical strategy across complex problem areas, balancing user relevance, ecosystem health, system scalability, and business impact.

• Enthusiasm for developing scalable, well-structured, and responsible AI solutions that generate business value.

• Strong interpersonal skills and a collaborative mindset, with the ability to communicate complex technical concepts effectively to diverse audiences and foster strong relationships with cross-functional partners.