Staff Machine Learning Engineer, Retrieval

Details

Reddit is seeking a Staff Machine Learning Engineer to provide technical leadership for the Ads Retrieval ML team. The team builds machine learning systems that identify relevant advertising candidates for Reddit users before downstream ranking and auction decisions. The work involves large-scale retrieval across multiple objectives, placements, and geographies, combining representation learning, candidate generation, nearest-neighbor search, behavioral and contextual signals, and offline and online experimentation.

This applied machine learning role focuses on retrieval modeling and end-to-end product impact. The successful candidate will work closely with data and objective design, model development, evaluation, experimentation, and production launches while setting technical 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 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 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, considering recall, relevance, freshness, diversity, coverage, latency, and cost trade-offs.
• Establish evaluation practices connecting retrieval metrics such as recall, precision, candidate coverage, and calibration to downstream lift, ads outcomes, and user outcomes.
• Lead offline analysis and online experiments, interpret ambiguous results, and translate findings into subsequent modeling iterations.
• Partner with downstream ranking, ads platform, auction, measurement, and product teams to integrate retrieval models into the full ads funnel.
• Write design documents, review code and model changes, and improve modeling, testing, observability, and production ownership.
• Mentor machine learning engineers and help grow expertise in retrieval, recommendation, and representation learning.

Requirements

• 7+ years of industry experience, including substantial experience building and shipping applied machine learning products.
• Deep experience with information retrieval, candidate generation, recommender systems, ranking, or related relevance problems.
• Strong understanding of retrieval modeling concepts, including deep neural networks, 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 machine learning 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, including setting direction, leading complex projects, influencing partner teams, and mentoring engineers.
• Excellent written and verbal communication skills, with the ability to explain complex modeling choices to technical and non-technical audiences.

Preferred Qualifications

• Experience with ads retrieval, 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 machine learning or ranking systems.
• Experience with sequential modeling, such as recurrent neural networks or Transformers.

Benefits

• 100% remote opportunity, with four office locations for hybrid or onsite work preferences in New York, San Francisco, Los Angeles, and Chicago.
• Comprehensive healthcare benefits and income replacement programs.
• 401(k) with employer match.
• Global benefit programs covering workspace, professional development, and caregiving support.
• Family planning support.
• Gender-affirming care.
• Mental health and coaching benefits.
• Flexible vacation and paid volunteer time off.
• Generous paid parental leave.

Compensation

The base salary range for this position is $230,000–$322,000 USD. The role is also eligible to receive equity in the form of restricted stock units and, depending on the position offered, may be eligible to receive a commission. Final offer amounts vary based on factors including skills, depth of work experience, and relevant licenses or credentials.

Reddit is an equal opportunity employer committed to building a workforce representative of the diverse communities it serves. The company provides reasonable accommodations for qualified individuals with disabilities and disabled veterans during the application and interview process. In select roles and locations, interviews may be recorded, transcribed, and summarized by artificial intelligence, with an opportunity to opt out before scheduled interviews.