Staff Machine Learning Engineer, Shopping Ads

Details

Reddit's Shopping Ads team builds relevant, performant, and scalable commerce advertising experiences that help advertisers connect products with people who are likely to find them useful. The Staff Machine Learning Engineer will lead the technical strategy and execution for models powering Shopping Ads delivery across Dynamic Product Ads and Product Listing Ads.

This is a hands-on technical leadership role focused on translating business goals into an end-to-end machine learning roadmap and delivering impact through multiple systems and teams.

Responsibilities

• Lead the machine learning strategy and architecture for Shopping Ads delivery across targeting, retrieval, ranking, engagement, conversion, and value optimization.
• Own end-to-end model development, including opportunity sizing, data and label design, feature engineering, model selection, offline evaluation, online experimentation, deployment, monitoring, and iteration.
• Build and optimize models for low-funnel advertiser objectives while maintaining relevance, user experience, marketplace health, and measurement quality.
• Develop feature and representation strategies connecting user intent, context, product catalog signals, advertiser signals, and historical interactions across multiple models in the delivery stack.
• Apply and adapt state-of-the-art machine learning approaches to production problems, selecting architectures based on measurable benefit.
• Design systems that balance prediction quality with online latency, throughput, reliability, operational complexity, and serving cost.
• Drive complex initiatives requiring coordinated changes across Shopping Ads, Catalog, Foundational Insights, ML Platform, Ads Serving, Auction, Bidding, Product, and Data Science teams.
• Set a high technical bar through architecture reviews, experimentation standards, production ownership, observability, and model-quality practices.
• Mentor engineers and technical leads, clarify ownership, and help the team execute effectively in ambiguous problem spaces.
• Stay current with advances in ads optimization, commerce recommendation, retrieval and ranking, representation learning, and production machine learning systems.

Requirements

• 7+ years of professional software or machine learning engineering experience, including substantial experience building applied machine learning systems in production.
• Demonstrated experience building end-to-end models or model-driven products that improve advertising, recommendation, search, or marketplace performance.
• Experience optimizing low-funnel objectives such as conversion, purchase value, revenue, return on ad spend, or other outcome-based metrics.
• Strong hands-on experience with model development, complex feature engineering, training and evaluation pipelines, online inference, and experimentation.
• A record of delivering complex results requiring multiple system components or teams to work together.
• Experience applying modern machine learning models in production and producing significant, measurable performance improvements.
• Proven technical leadership experience, including setting direction, driving architecture and execution, mentoring engineers, and influencing cross-functional stakeholders.
• Strong understanding of large-scale, high-throughput, low-latency machine learning systems and the trade-offs among model quality, latency, reliability, and cost.
• Excellent written and verbal communication, mentoring, and collaboration skills, with the ability to align teams on a long-term vision for Shopping Ads delivery.

Preferred Qualifications

• Experience with Shopping Ads, commerce ads, Dynamic Product Ads, Product Listing Ads, product recommendation, or retail media.
• Experience with targeting, candidate retrieval, ranking, conversion modeling, value optimization, recommender systems, or representation learning.
• Experience designing features or shared representations used across multiple models in a multi-stage delivery stack.
• Experience with deep learning architectures such as multi-task models, sequence models, transformers, two-tower models, graph methods, or learned embeddings.
• Experience with catalog quality, product feeds, advertiser-side signals, delayed or sparse conversion labels, and online/offline distribution shift.
• Experience at a large-scale ads, social, search, recommendation, e-commerce, or marketplace company.

Benefits

• Comprehensive healthcare benefits and income replacement programs.
• 401(k) with employer match.
• Global benefit programs supporting workspace, professional development, caregiving, and other lifestyle needs.
• Family planning support.
• Gender-affirming care.
• Mental health and coaching benefits.
• Flexible vacation and paid volunteer time off.
• Generous paid parental leave.
• Eligible to receive equity in the form of restricted stock units and, depending on the position offered, potentially a commission.
• U.S.-based employees may also receive medical, dental, and vision insurance, a 401(k) program with employer match, vacation time, and parental leave.

Compensation

The base salary range for this position is $230,000–$322,000 USD. The posting may span more than one career level, and final offers depend on factors including skills, depth of work experience, and relevant licenses or credentials.

Reddit is an equal opportunity employer committed to reasonable accommodations for qualified individuals with disabilities and disabled veterans.