Quick summary
- Work type
- Remote
- Location
- Remote
- Salary
- $253,300–$354,600 / yr
- Posted
- 90 days ago
- Freshness
- Confirmed live yesterday
Market check
Salary context
How this pay compares to similar roles
This role pays more than 89% of similar roles. Most pay $198,025–$269,637 — the blue band above. At the midpoint, this role pays about $304k versus about $234k 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 89 open roles on FindRole.
Listed pay typically runs $216,700–$303,000 across 89 roles with salary data.
Most-posted roles
- Software Engineer 27
- Machine Learning Engineer 10
- Product Manager 8
- Data Scientist 7
- Backend Engineer 3
At a glance
TL;DR · Machine Learning Manager, Feed Relevance (Retrieval)
The Machine Learning Manager, Feed Relevance (Retrieval) leads the Feed Retrieval team to build systems that identify, retrieve, and shape candidate inventory for personalized feeds. This role involves defining the technical vision and long-term roadmap for large-scale recommender systems while managing the day-to-day development of retrieval models. Key responsibilities include overseeing the design of systems that source diverse, high-quality content, establishing measurement and experimentation practices, and collaborating with infrastructure and ranking teams to ensure low-latency performance. The position requires deep expertise in production machine learning, specifically regarding retrieval models, embedding-based systems, sequence models, transformer-based architectures, and LLM-powered recommendation applications. The work focuses on expanding content variety and improving personalization for users across different signal levels within a large-scale content ecosystem.
Skills
What you'll do
- Define the technical vision and long-term roadmap for Feed Retrieval systems.
- Translate Feed Relevance goals into a prioritized team roadmap balancing model quality and infrastructure costs.
- Oversee the design and optimization of retrieval systems to source high-quality content candidates.
- Establish measurement, experimentation, and debugging practices to track retrieval quality and downstream impact.
- Build scalable, low-latency retrieval systems in collaboration with ML platform and infrastructure teams.
- Maintain high standards for system performance, reliability, latency, and cost efficiency.
- Recruit, interview, and hire diverse and talented machine learning engineers to grow the team.
What we're looking for
- 2+ years of experience building and managing high-performing ML or recommender-systems teams.
- Hands-on experience with large-scale production ML systems, including recommender systems, retrieval models, or LLM-powered applications.
- Strong understanding of candidate retrieval, embedding/indexing systems, ranking handoffs, and feed personalization.
- Ability to develop and communicate a clear technical strategy across ambiguous problem spaces.
- Experience with measurement, experimentation, and debugging practices for retrieval quality and downstream impact.
- Exceptional interpersonal skills to communicate complex technical topics to diverse audiences and cross-functional partners.
- Ability to coach and support the development of a team of machine learning engineers.
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