Staff Machine Learning Engineer, Shopping Ads

Reddit; United States; Remote; USD 230,000 - 322,000; Full time

Benefits offered by this job

Healthcare & Insurance

401k with Employer Match

Global benefits programs

Parental Leave

Mental Health & Coaching

Flexible Vacation

Job summary

Reddit is seeking a Staff Machine Learning Engineer for Shopping Ads to guide the technical strategy and execution for models powering ad delivery across targeting, retrieval, ranking, engagement, and conversion optimization.

You will own end-to-end model development from design to deployment, mentor engineers, and drive measurable improvements while coordinating across Shopping Ads, Catalog, ML Platform, and Data Science.

Qualifications

• 7+ years of professional software or ML engineering experience, including production ML systems.
• Experience building end-to-end models or model-driven products that improve advertising or marketplace performance.
• Experience optimizing low-funnel objectives such as conversion or revenue.
• Hands-on model development, feature engineering, training/evaluation pipelines, online inference.

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 and user experience.
• Develop feature and representation strategies that connect user intent, context, product catalog signals, advertiser signals, and historical interactions.
• Apply state-of-the-art ML approaches to production problems with architecture choices based on measurable benefit.
• Design systems balancing prediction quality with latency, throughput, reliability, and cost.
• Drive initiatives requiring coordination across multiple teams and domains.
• Set a high technical bar through reviews, ownership, and observability.
• Mentor engineers and leads, clarify ownership, and navigate ambiguous problems.
• Stay current with advances in ads optimization and production ML systems.

Skills

Machine learning engineering

End-to-end ML systems

Online experimentation

Model development

Leadership / mentoring

Low-latency ML systems

Cross-functional collaboration

Tools

TensorFlow

PyTorch

Spark

Job description

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 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com.

Reddit is a community of communities, built on shared interests, passion, and trust. Our 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.

As a Staff Machine Learning Engineer on Shopping Ads, you will lead the technical strategy and execution for the models that power Shopping Ads delivery. You will work across targeting, retrieval, ranking, engagement and conversion prediction, feature engineering, and online serving to improve advertiser outcomes across Dynamic Product Ads and Product Listing Ads. This is a hands-on technical leadership role for an engineer who can translate business goals into an end-to-end ML roadmap and deliver impact through multiple systems and teams.

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 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 rather than novelty alone.
• Design systems that balance prediction quality with online latency, throughput, reliability, operational complexity, and serving cost.
• Drive complex initiatives that require coordinated changes across Shopping Ads, Catalog, Foundational Insights, ML Platform, Ads Serving, Auction, Bidding, Product, and Data Science.
• 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 ML systems.

Minimum qualifications

• 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-overs 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 one or more of 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
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave

Pay Transparency:

This job posting may span more than one career level.

In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/.

To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below.

The base salary range for this position is:

$230,000 - $322,000 USD

In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews.

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