Staff Machine Learning Engineer, Ads Foundational Representations

redditsenior

En corto

→Desarrollar representaciones de entidades clave (usuarios, contenidos, anuncios) usando ML y modelos avanzados.
→Trabajar en embeddings multimodales, grafos de conocimiento y modelos basados en LLM para mejorar la relevancia publicitaria.
→Liderar proyectos técnicos de punta con impacto directo en el sistema de anuncios y productos de negocio.

Fluent English required for collaboration and documentation.

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Las preguntas que te van a hacer

1. ¿Cómo diseñarías un sistema de embeddings multimodales para posts y anuncios en Reddit usando LLMs y VLMs?

🔒 +7 preguntas más

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¿Qué piden?

✓Experiencia comprobada en ingeniería de machine learning a gran escala.
✓Conocimiento profundo en embeddings (textuales, multimodales, gráficos).
✓Habilidades sólidas en modelado secuencial y representaciones de usuarios.
✓Experiencia con LLMs, VLMs o modelos fundamentales aplicados a representaciones.
✓Capacidad para liderar técnicamente y mentorizar a otros ingenieros.
✓Experiencia en diseño y ejecución de proyectos de ML end-to-end.

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

PythonTensorFlowPyTorchSparkKubernetesDockerAirflowGCPPostgreSQLRedis

¿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 . Location: Reddit has a flexible first workforce. Don't live near our office? No worries: you can work remotely from anywhere in the UK or the Netherlands. The Ads Foundational Representations (AFR) team develops signals and representations of Reddit’s core entities (ads, posts, users, and so on), capturing the semantic, contextual, and behavioral information that Reddit Ads needs. We work on building embeddings to understand content and users' interests based on the content they engage with. Our team has the potential to highlight one of Reddit's biggest differentiators: genuinely curated, high-quality, extremely relevant, and daily updated organic content. We are a Machine Learning/Data heavy team with a focus on the following areas: • Multimodal & Content Embeddings - Make sense of organic (posts, comments, subreddits) and promoted (ads, shopping products, their landing pages) text and media content by embedding them into a shared space. • Contextual and Behavioral Relevance - Working with Product & Data Science, establishing definitions of what ads are relevant to users and the content we show them next to, building metrics and fine-tuning embeddings to better reflect relevance. • Knowledge Graph Embeddings - Building representations for the Knowledge graph entities, e.g., intellectual properties/brands, to be used for high-precision targeting & business insights. • User Intent Modeling - Leveraging various techniques to introduce user representations based on the content they interact with: batch & real-time sequence modeling, LLM summarization, etc. • LLM-based Representations - Leveraging LLMs, VLMs, and foundational models to build complex representations of Reddit entities that improve ranking outcomes The signals and features we create become a key piece in the Ads Delivery funnel, from targeting to the auction, as well as the Business Insights product and other advertiser-facing products such as Creative generation and optimization. As a Staff ML Engineer , you’ll be in charge of setting the technical direction of multiple pillars the team owns. You will lead cross-functional ML projects end to end - from high-level business gap analysis to engineering execution. Roughly 50% of your time will be spent on technical leadership and mentorship (driving strategy & designs, cross-functional collaboration, raising the quality bar), another 50% being individual hands-on work (data analysis & engineering, modeling & automation). Responsibilities • Providing technical leadership and mentorship to MLEs in the team: driving designs & their review, establishing best pract

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