Staff Machine Learning Engineer, Retrieval

Role overview

A Staff Machine Learning Engineer role focused on retrieval modeling for a large-scale advertising platform. The position centers on designing and evolving the candidate generation stage of the ads delivery funnel, ensuring that the right campaigns and creatives surface for users across multiple placements and geographies. It is a hands-on technical leadership role that combines applied modeling, experimentation, and mentorship within a high-volume environment.

Responsibilities

- Define the multi-year technical roadmap for ads retrieval modeling in collaboration with engineering, product, data science, and ads stakeholders. - Design, build, and ship candidate-generation and retrieval models that feed downstream ranking and auction systems. - Apply modern deep learning approaches such as two-tower architectures, embeddings, sequence models, and graph-based methods where they meaningfully improve relevance and advertiser outcomes. - Strengthen the retrieval stack across objectives, labels, hard-negative mining, feature design, embedding generation, candidate filtering, and retrieval depth decisions. - Build evaluation practices that connect recall, precision, coverage, calibration, and downstream lift to ads and user experience metrics, and translate experiment findings into the next modeling iteration. - Mentor ML engineers and raise the team's bar for modeling rigor, code quality, testing, observability, and production ownership.

Requirements

- 7+ years of industry experience with a substantial track record of shipping applied ML products to production. - Deep expertise in information retrieval, candidate generation, recommender systems, or large-scale ranking problems. - Strong command of retrieval modeling concepts including DNNs, embeddings, two-tower or dual-encoder architectures, approximate nearest-neighbor search, and multi-stage retrieval. - Proficiency training, evaluating, debugging, and deploying deep learning models using frameworks such as TensorFlow or PyTorch. - Demonstrated end-to-end ownership of ML projects, from problem framing and data preparation through offline evaluation, online experimentation, launch, and iteration. - Solid software engineering fundamentals and excellent communication skills, with the ability to explain complex modeling choices to both technical and non-technical audiences.

Nice to have

- Experience with ads retrieval, ad serving, marketplace optimization, or search relevance at scale. - Background modeling user, content, campaign, or ad interactions using sequential, graph, or multimodal signals, including Transformer- or RNN-based architectures. - Publications, patents, or notable industry contributions in applied ML, retrieval, or ranking systems.

Benefits and work setup

- 100% remote opportunity, with optional hybrid or onsite work at offices in New York, San Francisco, Los Angeles, and Chicago. - Comprehensive healthcare coverage and income replacement programs, plus family planning, gender-affirming care, and mental health and coaching benefits. - 401(k) program with employer match. - Flexible vacation, paid volunteer time off, and generous paid parental leave. - Equity in the form of restricted stock units, with commission eligibility depending on the position. - U.S.-base salary range of $230,000 to $322,000, benchmarked by function, level, and location against comparable growth-stage companies.