Staff Machine Learning Engineer, Retrieval & Targeting

Reddit, Inc.

. Define the technical direction and multi-year roadmap for ads retrieval modeling with engineering, product, data science, and ads stakeholders .

Posted 10/23/2026full-timeRemote • United StatesLead💰 $230,000 - $322,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in defining technical direction and developing advanced retrieval models for advertising, utilizing deep learning techniques and strong experimental design. Proven ability to lead projects, mentor teams, and connect model performance to user outcomes.

Highest-signal resume keywords

Deep Learning Model DevelopmentInformation Retrieval ExpertiseTechnical Leadership ExperienceExperimental Design and EvaluationProduction Code Quality

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningInformation RetrievalCandidate GenerationRecommender SystemsDeep Neural NetworksEmbeddingsTwo-Tower ModelsApproximate Nearest-Neighbor SearchModel EvaluationFeature Engineering

Soft Skills

Excellent CommunicationMentoring

Tools & Technologies

TensorFlowPyTorchVector Retrieval SystemsGraph-Based MethodsSequence Models

Industry Keywords

Ads RetrievalAds RankingAd TargetingAd ServingSearch RelevanceMarketplace Optimization

Tech Stack

Tools & technologies

PyTorchTensorflow

About the role

Key responsibilities & impact

Define the technical direction and multi-year roadmap for ads retrieval modeling 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 two-tower architectures, representation learning, embeddings, sequence models, graph-based methods, and other deep learning techniques
Improve 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, balancing recall, relevance, freshness, diversity, coverage, latency, and cost
Establish evaluation practices connecting retrieval metrics to ads and user outcomes
Lead offline analysis and online experiments and translate findings into modeling iterations
Partner with downstream ranking, ads platform, auction, measurement, and product teams
Write design documents, review code and model changes, and improve modeling, testing, observability, and production ownership
Mentor ML engineers and grow team expertise in retrieval, recommendation, and representation learning

Requirements

What you’ll need

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 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
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
Experience with large-scale behavioral, contextual, or content datasets and complex feature pipelines
Strong software engineering fundamentals and ability to write clear, reliable, maintainable production code
Technical leadership experience, including setting direction, leading complex projects, influencing partner teams, and mentoring engineers
Excellent written and verbal communication
Preferred: experience with ads retrieval, ads ranking, ad targeting, ad serving, recommendation, search relevance, or marketplace optimization
Preferred: experience modeling interactions with sequential, graph, or multimodal signals
Preferred: experience connecting retrieval improvements to ranking, auction, conversion, revenue, or user-experience outcomes
Preferred: experience in ads marketplaces at peer companies
Preferred: publications, patents, or industry contributions in applied ML or ranking systems
Preferred: experience with sequential modeling such as RNNs or Transformers

Benefits

Comp & perks

100% remote opportunity
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
Equity in the form of restricted stock units
Potential commission eligibility depending on the position offered
Medical, dental, and vision insurance
Interview recording opt-out option
Reasonable accommodations for qualified individuals with disabilities and disabled veterans