Staff Machine Learning Engineer, Ads Retrieval at Reddit

Confirmed open

Lead retrieval and targeting models for Reddit ads using embeddings, candidate generation and nearest-neighbor search. This applied machine-learning role is remote within the United States.

Reddit

Location: Remote
Work arrangement: remote
Technologies: PyTorch, TensorFlow

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Role overview

Reddit’s Ads Retrieval ML team selects relevant advertising candidates before ranking and auction. This Staff-level role leads applied retrieval modeling from data and objective design through experiments, launch and iteration. The posting specifies remote work within the United States.

Responsibilities

Set technical direction for ads retrieval and build candidate-generation models across placements and objectives.
Apply representation learning, embeddings, two-tower or other deep-learning methods where they improve relevance.
Tune retrieval objectives, labels, sampling, hard negatives, features and candidate filtering, balancing recall, freshness, latency and cost.
Lead offline evaluation and online experiments; integrate models with ranking, auction and measurement teams and mentor engineers.

Qualifications

At least seven years of industry experience with substantial applied ML product delivery.
Deep experience in information retrieval, recommender systems or ranking, including DNNs, embeddings and approximate nearest-neighbor search.
Production model training and evaluation with PyTorch, TensorFlow or similar tools, strong software engineering and technical leadership.

Technology in this role

PyTorch: Production model training and evaluation with PyTorch, TensorFlow or similar tools, strong software engineering and technical leadership.

Benefits

The US posting lists remote work, healthcare, retirement matching, flexible vacation, parental leave and other support programmes.

About Reddit

Reddit is a community platform with more than 100,000 active communities. Its machine-learning teams support discovery, relevance, advertising and marketplace experiences.

How to apply

Review the original posting and apply through the employer’s careers page.

View source and apply

Source checked: 2026-10-29

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