This is a high-impact team driving DAU growth and long-term user retention by connecting users to what matters most to them. If applying ML / AI in production to improve the relevance of Reddit Notifications excites you, then you’ve found the right place.

Responsibilities:

• Lead the team that architects and designs notifications relevance at Reddit.
• Guide team on holistic, adaptive systems covering budgeting optimization, candidate retrieval, and ranking.
• Work with ML engineers to design, implement, and optimize machine-learning models that drive personalization and user re-engagement.
• Participate in the full development cycle: design, develop, QA, experiment, analyze, and deploy.
• Build and maintain a diverse team that can collaborate across disciplines to find technical solutions to complex challenges.
• Serve as a thought partner to product and upper management to ensure your team’s plans align with company goals.
• Communicate your team’s work and set expectations with cross-functional stakeholders.
• Help your engineers identify career goals and create development plans to achieve them.
• Constantly seek opportunities to push your engineers & managers outside their comfort zone and turn followers into leaders.

Requirements:

• 2+ years of experience building and managing engineering teams.
• 5+ years of experience as a Machine Learning Engineer or Software Engineer working on large-scale machine learning systems.
• Deep understanding of building and deploying large-scale recommender systems (retrieval + ranking) in production.
• Hands-on experience working with deep learning models, sequential features and real-time systems.
• Experience with distributed training and inference using tools like Ray, PyTorch Distributed, or similar.
• Familiarity with reinforcement learning or multi-objective optimization in recommendation systems.
• Entrepreneurial and self-directed, innovative, results-oriented, biased towards action in fast-paced environments.
• Able to communicate and discuss complex topics with technical and non-technical audiences.
• Able to tackle ambiguous and undefined problems

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