Staff Machine Learning Engineer, Retrieval

Reddit·Remote - United StatesRemoto

En corto

→Diseñar y lanzar modelos de recuperación de anuncios a gran escala en Reddit.
→Trabajar en arquitecturas de deep learning como dos torres, representaciones y búsquedas vecinas próximas.
→Liderar técnicamente el impacto del modelo en relevancia, resultados de anunciantes y experiencia de usuario.

Fluent English required for collaboration across global teams.

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¿Qué piden?

✓Experiencia en diseño y lanzamiento de modelos de recuperación a gran escala.
✓Dominio de técnicas de deep learning: dos torres, embeddings, modelos de secuencia.
✓Habilidades sólidas en evaluación offline y experimentos online.
✓Experiencia con sistemas de recuperación basada en vecinos próximos (ANN).
✓Capacidad para liderar técnicamente equipos de ingenieros ML.
✓Habilidades de comunicación y colaboración con equipos de producto y plataformas.

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

two-tower architecturesrepresentation learningembeddingssequence modelsgraph-based methodsdeep learningapproximate nearest-neighborvector retrievaloffline analysisonline experiments

¿A quién escribirle en Reddit?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . Team Description: The Ads Retrieval ML team builds the machine learning systems that identify relevant advertising candidates for Reddit users. Retrieval sits at the heart of the ads delivery funnel: before downstream ranking and auction decisions, our models determine which campaigns and ads are eligible to compete. We work on large-scale retrieval across multiple objectives, placements, and geographies. Our work combines representation learning, candidate generation, nearest-neighbor search, behavioral and contextual signals, and rigorous offline and online experimentation. Role Description: We are looking for a Staff Machine Learning Engineer to provide technical leadership for the Retrieval ML team. You will lead the design and evolution of retrieval models and modeling practices that improve relevance, advertiser outcomes, and user experience at Reddit scale. This is an applied ML role centered on retrieval modeling and end-to-end product impact. You will be expected to stay close to the technical details—from data and objective design through model development, evaluation, experimentation, and launch—while setting 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 modern approaches such as 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 key modeling decisions, including 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, reasoning about recall, relevance, freshness, diversity, coverage, latency, and cost trade-offs. • Establish strong evaluation practices that connect retrieval metrics—such as recall, precision, candidate coverage, calibration, and downstream lift—to ads and user outcomes. • Lead offline analysis and online experiments, interpret ambiguous results, and translate findings into the next modeling iteration. • Partner with downstream ranking, ads platform, auction, measurement, and product teams to ensure retrieval models integrate effectively into the full ads funnel. • Write design documents, review code and model changes, and raise the quality bar for modeling, testing, observability, and production ownership. • Mentor ML engineers and help grow the team’s expertise in retrieval, recommendation

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