Staff Machine Learning Engineer, Shopping Ads
R
Reddit, Inc.Ads Engineering
Remote - United StatesFull-TimeStaff
Salary$230,000 — $322,000 USD
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Job Details
- Experience
- 7+ years of professional software or machine learning engineering experience
- Required Skills
- PythonMachine LearningDeep Learning
Requirements
- 7+ years of professional software or machine learning engineering experience, including substantial experience building applied ML 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.
- Record of delivering complex results that require multiple system components or teams to work together.
- Experience applying modern machine learning models in production and producing significant, measurable performance improvements.
- Proven technical-lead experience: setting direction, driving architecture and execution, mentoring engineers, and influencing cross-functional stakeholders.
- Strong understanding of large-scale, high-throughput, low-latency ML systems and the trade-offs among model quality, latency, reliability, and cost.
- Excellent written and verbal communication, mentoring, and collaboration skills.
Responsibilities
- Lead the ML strategy and architecture for Shopping Ads delivery across targeting, retrieval, ranking, engagement, conversion, and value optimization.
- Own end-to-end model development from 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 strong relevance, user experience, marketplace health, and measurement quality.
- Develop feature and representation strategies that connect user intent, context, product catalog signals, advertiser signals, and historical interactions.
- Design systems that balance prediction quality with online latency, throughput, reliability, operational complexity, and serving cost.
- Drive complex initiatives that require coordinated changes across internal teams such as Shopping Ads, Catalog, Foundational Insights, and ML Platform.
- Set a high technical bar through architecture reviews, experimentation standards, production ownership, and model-quality practices.
- Mentor engineers and technical leads, clarify ownership, and help the team execute effectively in ambiguous problem spaces.
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