This role pays more than
91%
of similar roles. Most pay
$196,562–$260,254
— the shaded band above.
At the midpoint, this role pays about
$304k
versus about
$228k
for comparable roles.
Based on 240 similar postings.
Employer
About Reddit
Reddit is a social news aggregation and discussion platform where users share content, vote on posts, and engage in community conversations across thousands of interest-based forums called subreddits.
Reddit currently has
77 open roles
on FindRole.
Listed pay typically runs
$217,000–$303,400
across 77 roles with salary data.
Machine Learning Manager, Feed Relevance (Retrieval) leads a high-impact team of Machine Learning Engineers focused on building systems that identify, retrieve, and shape candidate inventory for personalized feeds. The role involves defining technical vision and roadmaps, managing project prioritization, coaching engineers, and overseeing the design of retrieval systems to source diverse, fresh content. Key responsibilities include establishing measurement practices, collaborating with infrastructure and ranking teams, and ensuring operational excellence in performance and cost efficiency. Candidates must possess expertise in large-scale production machine learning, specifically regarding recommender systems, embedding-based systems, sequence models, transformer-based architectures, and LLM-powered recommendation applications. The work centers on the technical challenge of improving personalization and discovery by expanding high-quality content options for users across varying levels of signal within a complex feed relevance ecosystem.
Define the technical vision and long-term roadmap for Feed Retrieval systems aligned with business objectives.
Translate broad feed relevance goals into a prioritized team roadmap balancing model quality and infrastructure costs.
Oversee the design, development, and optimization of retrieval systems to source high-quality content candidates.
Establish measurement, experimentation, and debugging practices to evaluate retrieval quality and downstream impact.
Collaborate with platform, infrastructure, and ranking teams to build scalable, low-latency machine learning systems.
Maintain high standards for system performance, reliability, cost efficiency, and responsible recommendation practices.
Coach and develop the skills of a team of Machine Learning Engineers.
Partner with recruiting teams to attract, interview, and hire diverse machine learning talent.
What we're looking for
You must have at least 2 years of experience building and managing high-performing ML or recommender systems teams.
You must have hands-on experience with large-scale production ML systems including retrieval models, embedding-based systems, and transformer-based architectures.
You must possess deep knowledge of recommender systems, specifically candidate retrieval, ranking handoffs, and feed personalization.
You must be able to develop and communicate technical strategies across ambiguous problem spaces while balancing scalability and business impact.
You must have strong interpersonal skills to communicate complex technical topics to diverse audiences and cross-functional partners.
You must be capable of coaching, developing, and recruiting a team of machine learning engineers.
Machine Learning
Recommender Systems
LLM
Data Science
Infrastructure
Content Understanding
Candidate Retrieval
Ranking
Personalization
Responsible AI
System Performance
Measurement Strategies