below marketmost similar roles pay hereabove market
This role pays more than
93%
of similar roles. Most pay
$208,006–$267,412
— the blue band above.
At the midpoint, this role pays about
$319k
versus about
$238k
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.
The Senior Machine Learning Manager, Video Ranking will lead the Media Ranking team to improve relevance, personalization, and discovery of core product content. In this role, you will define the technical vision and long-term roadmap for Feed Relevance while coaching a team of machine learning engineers. You will oversee the design, development, and optimization of sophisticated content distribution systems and collaborate with platform teams to build next-generation AI-powered solutions. Key responsibilities include managing large-scale production ML systems, specifically focusing on transformer-based architectures, sequence models, and Large Language Models. You must possess deep domain knowledge in recommender systems, including candidate retrieval, ranking, value modeling, and ecosystem dynamics. This position solves the technical challenge of integrating video and audio as core components of the user experience.
Define the technical vision and long-term roadmap for Feed Relevance and ML solutions.
Oversee the design, development, and optimization of relevance, discovery, and content distribution systems.
Coach and support the professional development of a team of Machine Learning Engineers.
Partner with recruiting to attract, interview, and hire diverse and talented machine learning engineers.
Collaborate with platform and infrastructure teams to develop next-generation AI-powered solutions.
Maintain high standards for system performance, reliability, and efficiency according to responsible AI principles.
Work with cross-functional partners to identify opportunities and communicate team progress.
What we're looking for
5+ years of experience building and managing high-performing ML teams.
Hands-on experience with large-scale production ML systems, including transformer-based architectures, sequence models, or Large Language Models (LLMs).
Strong understanding of recommender systems, including candidate retrieval, ranking, value modeling, ecosystem dynamics, and measurement strategies.
Ability to develop and communicate a clear technical strategy that supports broader company objectives.
Experience developing scalable, well-designed, and responsible AI solutions that drive business value.
Strong interpersonal skills to communicate complex technical topics to diverse audiences and build cross-functional relationships.
Machine Learning
Recommender Systems
LLM
Transformer
Embedding-based Systems
Sequence Models
Retrieval Systems
Ranking
Production ML
AI
Infrastructure
Experimentation
Measurement Strategies
Machine Learning
Recommender Systems
LLM
Production ML
Candidate Retrieval
Ranking
Personalization
Cold-start Modeling
Content Understanding
Responsible AI
Data Science
Machine Learning
Deep Learning
Ranking Systems
Recommendation Systems
Generative Recommendations
Graph Representation Learning
AI
Production Reliability
Multi-modal Content Understanding
Algorithms
Python
PyTorch
C++
Generative AI
Computer Vision
3D Computer Vision
Diffusion Models
3D Gaussian Splatting
Neural Radiance Fields
Differentiable Rendering
Distributed Training
On-device ML
Scene Reconstruction
Depth Estimation
Multi-view Geometry
Novel View Synthesis
Machine Learning
Computer Vision
Python
PyTorch
TensorFlow
Deep Learning
Multimodal Learning
Video Understanding
Distributed Systems
Data Processing
Embeddings
Retrieval
Fine-tuning
Machine Learning
Large Language Models
TensorFlow
PyTorch
Spark ML
scikit-learn
Caffe2
Distributed Systems
Information Retrieval
Ranking Systems
System Design
Indexing