Ads Conversion Modeling, Machine Learning Engineering Manager

Details

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 121 million daily active unique visitors, Reddit is one of the internet’s largest sources of information.

The Conversion Modeling Team develops and maintains machine learning models that drive user conversions from Reddit Ads, focusing on predictive modeling for interactions like purchase, signup, add to cart, and other lower-funnel user actions. As the team expands its ML infrastructure and incorporates new engagement signals, Reddit is hiring an Engineering Manager to define vision, set technical strategy, and execute a roadmap for conversion modeling.

This role is remote-friendly and open to candidates anywhere in the United States.

Target Skills and Expertise

• Model Architectures: Expertise in architecting and implementing deep learning models, with experience in ranking, recommendation, or conversion modeling.
• ML Frameworks: Proficiency with mainstream ML libraries (TensorFlow, PyTorch).
• End-to-End ML Lifecycle: Experience in training, testing, and deploying production-grade machine learning models.
• Data Pipelines: Experience orchestrating large-scale data generation and processing pipelines.
• Ads Domain Experience: Experience with interaction of ranking models with other Ads systems like bidding, auction, retrieval, etc.
• Ads Modeling (Preferred): Background in ads modeling or familiarity with engagement prediction models in the ads domain is beneficial.

Responsibilities

• Define the team’s vision, set strategic direction, and execute a technical roadmap for conversion modeling at Reddit.
• Oversee the model development lifecycle from ideation to deployment, ensuring high standards of ML performance and robustness.
• Recruit, mentor, and retain ML engineering talent; provide technical mentorship and foster team growth.
• Collaborate with PMs, data scientists, and other engineering teams to align on engagement strategies, data requirements, and model KPIs.
• Innovate and optimize ML architectures tailored to conversion prediction, leveraging deep learning and advanced ML techniques.
• Balance innovative modeling approaches with business objectives when planning and executing the roadmap.

Requirements

• At least 2+ years of experience building and managing high-performing machine learning teams; tech lead experience will also be considered.
• Deep hands-on experience working with machine learning models and deploying them in large-scale production systems, including training, evaluating, and deploying large-scale models.
• Technical domain knowledge: experience with ads conversion modeling, ranking (heavy ranker experience), and recommendations.
• Strategic thinking and ability to develop and communicate a clear technical strategy aligned with company objectives.
• Impact-driven mindset focused on scalable, well-designed, and responsible AI solutions.
• Exceptional communication and collaboration skills; ability to communicate complex technical topics to diverse audiences and build strong cross-functional relationships.

Perks and Benefits

• 100% remote opportunity (4 office locations available for hybrid/onsite preference: New York City, San Francisco, Los Angeles, and Chicago).
• Competitive salary and equity options.
• Comprehensive health benefits (medical, dental, vision) and workplace perks (home office setup stipend, etc.).
• Generous 401(k) matching.
• Flexible vacation policy.
• Paid parental leave (4+ months) and family planning support.
• Paid volunteer time off.

Pay Transparency

Base salary range for U.S.-based candidates: $230,000 - $322,000 USD. This position may also be eligible for equity in the form of restricted stock units and, depending on the position, commission. Final offers depend on skills, experience, and other factors.