Staff Machine Learning Engineer, Retrieval & Targeting

Location: Remote - United States; 100% remote opportunity. Four office locations are available for hybrid or onsite work preference in New York, San Francisco, Los Angeles, and Chicago.

Base salary: $230,000–$322,000 USD per year, plus potential equity and, depending on the position, commission.

Company

Reddit is a community platform with more than 100,000 active communities and approximately 130 million daily active unique visitors.

What you will do

• Set 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.
• Improve objectives, labels, sampling, hard-negative mining, feature design, embedding generation, candidate filtering, and retrieval depth.
• Develop retrieval systems using representation learning, two-tower architectures, sequence models, graph-based methods, deep learning, approximate nearest-neighbor search, and vector retrieval.
• Establish offline and online evaluation practices covering recall, precision, candidate coverage, calibration, downstream lift, relevance, freshness, diversity, latency, and cost.
• Partner with ranking, ads platform, auction, measurement, and product teams; review code and model changes; write design documents; and mentor ML engineers.

Requirements

• 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 with TensorFlow, PyTorch, or similar frameworks.
• Experience owning 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 relationships between offline retrieval metrics and downstream business and user metrics.
• 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 in setting direction, leading complex projects, influencing partner teams, and mentoring engineers, together with excellent written and verbal communication.

Nice to have

• 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-experience outcomes.
• Experience in ads marketplaces at peer companies.
• Publications, patents, or industry contributions in applied ML or ranking systems.
• Experience with sequential modeling such as RNNs or Transformers.

Culture & Benefits

• 100% remote work, with optional hybrid or onsite work at offices in New York, San Francisco, Los Angeles, and Chicago.
• Healthcare benefits, income replacement programs, and a 401(k) with employer match.
• Global benefit programs covering workspace, professional development, and caregiving support.
• Family planning support, gender-affirming care, and mental health and coaching benefits.
• Flexible vacation, paid volunteer time off, and generous paid parental leave.
• Interviews may be recorded, transcribed, and summarized by AI; candidates can opt out before scheduled interviews.