Staff Machine Learning Engineer, Shopping Ads

Reddit · Remote - United StatesRemotolead

En corto

▸Liderazgo técnico en modelos de machine learning para anuncios de comercio en Reddit.
▸Desarrollas y optimizas modelos desde el diseño hasta la producción, con enfoque en resultados de anunciantes.
▸Rol clave que impacta múltiples sistemas y equipos en la cadena de entrega de anuncios.

Fluent English required

En ~1 minuto te damos: quién te entrevista, las preguntas probables con respuestas desde tu CV, y tu CV adaptado a esta vacante. Gratis, sin tarjeta.

¿Qué piden?

✓7+ años de experiencia en ingeniería de ML en producción.
✓Experiencia comprobada en modelos end-to-end para publicidad, recomendación o búsqueda.
✓Capacidad de liderar arquitectura y estrategia ML en entornos complejos.
✓Habilidades avanzadas en feature engineering y representaciones de datos.
✓Experiencia en sistemas de alto rendimiento, bajo latencia y escalables.
✓Capacidad de colaborar con múltiples equipos técnicos e impulsar iniciativas cruzadas.

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

Machine LearningModel DevelopmentFeature EngineeringOffline EvaluationOnline ExperimentationModel DeploymentModel MonitoringOnline ServingAuctionBidding

¿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 . Reddit is a community of communities, built on shared interests, passion, and trust. Our Shopping Ads team builds relevant, performant, and scalable commerce advertising experiences that help advertisers connect products with people who are likely to find them useful. As a Staff Machine Learning Engineer on Shopping Ads, you will lead the technical strategy and execution for the models that power Shopping Ads delivery. You will work across targeting, retrieval, ranking, engagement and conversion prediction, feature engineering, and online serving to improve advertiser outcomes across Dynamic Product Ads and Product Listing Ads. This is a hands-on technical leadership role for an engineer who can translate business goals into an end-to-end ML roadmap and deliver impact through multiple systems and teams. Responsibilities • Lead the ML strategy and architecture for Shopping Ads delivery across targeting, retrieval, ranking, engagement, conversion, and value optimization. • Own end-to-end model development from opportunity sizing, data and label design, feature engineering, model selection, offline evaluation, online experimentation, deployment, monitoring, and iteration. • Build and optimize models for low-funnel advertiser objectives while maintaining strong relevance, user experience, marketplace health, and measurement quality. • Develop feature and representation strategies that connect user intent, context, product catalog signals, advertiser signals, and historical interactions across multiple models in the delivery stack. • Apply and adapt state-of-the-art machine learning approaches to production problems, selecting architectures based on measurable benefit rather than novelty alone. • Design systems that balance prediction quality with online latency, throughput, reliability, operational complexity, and serving cost. • Drive complex initiatives that require coordinated changes across Shopping Ads, Catalog, Foundational Insights, ML Platform, Ads Serving, Auction, Bidding, Product, and Data Science. • Set a high technical bar through architecture reviews, experimentation standards, production ownership, observability, and model-quality practices. • Mentor engineers and technical leads, clarify ownership, and help the team execute effectively in ambiguous problem spaces. • Stay current with advances in ads optimization, commerce recommendation, retrieval and ranking, representation learning, and production ML systems. Minimum qualifications • 7+ years of professional software or machine learning engineering experience, including substantial experience building applied ML systems in production. • Demonstrated experience building end-to-end models or model-driven products that improve advertising, recommendation, search, or marketpla

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