S Staff Machine Learning Engineer, Retrieval & Targeting Remote - United States, US Principal PyTorch TensorFlow machine learning

ContentBuffer; United States; Remote; USD 180,000 - 260,000; Full time

Job summary

Reddit is seeking a Staff Machine Learning Engineer to lead the Ads Retrieval ML team. You will steer retrieval model design, experimentation, and impact at Reddit scale. Expect hands-on work from data design to production, with mentoring of engineers and cross-functional collaboration with product and ads teams.

You will guide candidate-generation, retrieval strategies, and advanced modeling approaches, balancing recall, relevance, and latency while delivering measurable product outcomes.

Qualifications

• 7+ years delivering applied ML products in industry.
• Deep experience with information retrieval and candidate generation.
• Expertise in retrieval modeling concepts and multi-stage retrieval.
• Strong experience training, evaluating, debugging, and deploying DL models with TensorFlow or PyTorch.
• Ownership of ML projects from problem framing to production launch.
• Strong experimental design and understanding of offline vs online metrics.
• Experience with large-scale behavioral datasets and complex feature pipelines.
• Excellent software engineering fundamentals and production-grade code.

Responsibilities

• Define the technical direction and multi-year roadmap for ads retrieval modeling.
• Design, develop, and launch candidate-generation and retrieval models across Reddit surfaces.
• Apply advanced techniques like two-tower architectures, embeddings, and graph methods.
• Improve retrieval stack decisions: objectives, sampling, hard negatives, features, and depth.
• Collaborate on recall, relevance, latency, cost trade-offs with NN search systems.
• Establish evaluation practices linking retrieval metrics to ads and user outcomes.
• Lead offline analysis, online experiments, and translate findings to iterations.
• Partner with downstream ranking and ads platform teams for integration.
• Mentor engineers and raise the team's expertise in retrieval and representation learning.

Skills

Staff-level leadership

Information retrieval

Recommender systems

DNN / embeddings

NN search / ANN

TensorFlow / PyTorch

Model evaluation & experimentation

Production ML engineering

Large-scale systems

Technical leadership

Code quality / testing

Cross-functional collaboration

Tools

TensorFlow

PyTorch

Index/ANN systems

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.

Team Description:

The Ads Retrieval ML team builds the machine learning systems that identify relevant advertising candidates for Reddit users. Retrieval sits at the heart of the ads delivery funnel: before downstream ranking and auction decisions, our models determine which campaigns and ads are eligible to compete. We work on large-scale retrieval across multiple objectives, placements, and geographies. Our work combines representation learning, candidate generation, nearest-neighbor search, behavioral and contextual signals, and rigorous offline and online experimentation.

Role Description:

We are looking for a Staff Machine Learning Engineer to provide technical leadership for the Retrieval and Targeting ML team. You will lead the design and evolution of retrieval models and modeling practices that improve relevance, advertiser outcomes, and user experience at Reddit scale. This is an applied ML role centered on retrieval modeling and end-to-end product impact. You will be expected to stay close to the technical details—from data and objective design through model development, evaluation, experimentation, and launch—while setting direction for other engineers.

Responsibilities:

• Define the technical direction and multi-year roadmap for ads retrieval modeling in partnership with engineering, product, data science, and ads stakeholders.
• Design, develop, and launch candidate-generation and retrieval models for campaigns and ads across Reddit’s advertising surfaces.
• Apply modern approaches such as two-tower architectures, representation learning, embeddings, sequence models, graph-based methods, and other deep learning techniques when they create meaningful product value.
• Improve the retrieval stack across key modeling decisions, including objectives, labels, sampling strategies, hard-negative mining, feature design, embedding generation, candidate filtering, and retrieval depth.
• Work with approximate nearest-neighbor and vector retrieval systems, reasoning about recall, relevance, freshness, diversity, coverage, latency, and cost trade-offs.
• Establish strong evaluation practices that connect retrieval metrics—such as recall, precision, candidate coverage, calibration, and downstream lift—to ads and user outcomes.
• Lead offline analysis and online experiments, interpret ambiguous results, and translate findings into the next modeling iteration.
• Partner with downstream ranking, ads platform, auction, measurement, and product teams to ensure retrieval models integrate effectively into the full ads funnel.
• Write design documents, review code and model changes, and raise the quality bar for modeling, testing, observability, and production ownership.
• Mentor ML engineers and help grow the team’s expertise in retrieval, recommendation, and representation learning.

Required Qualifications:

• 7+ years of industry experience, including substantial experience building and shipping applied ML products.
• Deep experience with information retrieval, candidate generation, recommender systems, ranking, or related relevance problems.
• Strong understanding of retrieval modeling concepts, including DNN, embeddings, two-tower or dual-encoder models, approximate nearest-neighbor search, and multi-stage retrieval.
• Deep experience training, evaluating, debugging, and deploying deep learning models using TensorFlow, PyTorch, or similar frameworks.
• Demonstrated ownership of ML projects from problem framing and data preparation through offline evaluation, online experimentation, production launch, and iteration.
• Strong command of experimental design and model evaluation, including how offline retrieval metrics relate to downstream business and user metrics.
• Experience working with large-scale behavioral, contextual, or content datasets and complex feature pipelines.
• Strong software engineering fundamentals and the ability to write clear, reliable, maintainable production code.
• Technical leadership experience: setting direction, leading complex projects, influencing partner teams, and mentoring other engineers.
• Excellent written and verbal communication, with the ability to explain complex modeling choices to technical and non-technical audiences.

Preferred Qualifications:

• Experience with ads retrieval, ads ranking, ad targeting, ad serving, recommendation, search relevance, or marketplace optimization
• Experience modeling user, content, campaign, or ad interactions with sequential, graph, or multimodal signals
• Experience connecting retrieval improvements to downstream ranking, auction, conversion, revenue, or user-experien