Staff Machine Learning Engineer, Retrieval & Targeting

3d•$230,000 - $322,000•Remote

About The Position

Reddit is hiring a Staff Machine Learning Engineer to provide technical leadership for the Ads Retrieval and Targeting ML team. The team builds machine learning systems that identify relevant advertising candidates for Reddit users, across multiple objectives, placements, and geographies. This applied ML role focuses on retrieval modeling and end-to-end product impact, including data and objective design, model development, evaluation, experimentation, and launch. The engineer will set technical direction while remaining close to implementation and helping other engineers grow their expertise.

Requirements

At least 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, 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.
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 the relationship 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 setting direction, leading complex projects, influencing partner teams, and mentoring engineers.
Excellent written and verbal communication skills, including explaining complex modeling choices to technical and non-technical audiences.

Nice To Haves

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.

Responsibilities

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 suitable approaches such as two-tower architectures, representation learning, embeddings, sequence models, graph-based methods, and other deep learning techniques.
Improve retrieval 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 advertising and user outcomes.
Lead offline analyses and online experiments, interpret results, and use findings to guide modeling iterations.
Partner with downstream ranking, ads platform, auction, measurement, and product teams to integrate retrieval models into the ads funnel.
Write design documents, review code and model changes, and improve modeling, testing, observability, and production ownership.
Mentor ML engineers and develop team expertise in retrieval, recommendation, and representation learning.

Benefits

Comprehensive healthcare benefits and income replacement programs.
401(k) with employer match.
Global benefit programs supporting workspace, professional development, and caregiving.
Family planning support.
Gender-affirming care.
Mental health and coaching benefits.
Flexible vacation and paid volunteer time off.
Generous paid parental leave.
Equity in the form of restricted stock units; depending on the position offered, commission may also be available.
Medical, dental, and vision insurance for U.S.-based employees.