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Staff Machine Learning Engineer, Retrieval

Reddit· Ads Product & Engineering
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Remote$230k - $322kfull-timePosted 4w ago

About the role

Staff-level ML engineer leading the Retrieval ML team focused on candidate generation and retrieval modeling for Reddit ads, with end-to-end ownership from design to production and impact.

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.

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.

Skills

Required:Ads retrieval modelingInformation retrievalDeep learning frameworksTwo-tower/dual-encoder modelsExperimentation and evaluation

Benefits

  • 100% remote opportunity (we have 4 office locations for hybrid/onsite work preference in NY, SF, LA and Chicago)
  • 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
Last updated Oct 7, 2026. Interested? Apply on the company site.
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