Software Engineer, Research Data Platform

Anthropic · San Francisco, CA | New York City, NYmid

En corto

▸Construyes herramientas de datos para investigadores de IA, transformando datos de entrenamiento en sistemas queryables.
▸Trabajas junto a equipos de investigación, entendiendo sus flujos y creando APIs, interfaces y catálogos de datos.
▸Destacado: el puesto prioriza impacto y colaboración directa con usuarios técnicos, sin exigir experiencia previa en ML.

Fluent English is required for collaboration with global teams.

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?

✓Experiencia significativa en ingeniería de software, especialmente en aplicaciones intensivas en datos o herramientas internas.
✓Capacidad para trabajar directamente con usuarios técnicos, recopilar requerimientos iterativamente y entregar soluciones adoptadas.
✓Enfoque orientado a resultados, con flexibilidad y prioridad en el impacto.
✓Disposición para asumir responsabilidades más allá del rol titular.
✓Interés genuino en aprender sobre investigación de machine learning.
✓Cuidado por los impactos sociales de la tecnología desarrollada.

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

PythonSQLSparkKafkaPostgreSQLS3DockerKubernetesFastAPIReact

¿A quién escribirle en Anthropic?

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

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role The Research Data Platform team builds the tools that Anthropic's researchers use every day to manage, query, and analyze the data that goes into training and evaluating frontier models. We power the internal applications researchers rely on to monitor RL runs, explore finetuning datasets, and understand what's happening inside their experiments. We're looking for engineers who love working directly with users and who excel at building data products — the pipelines that move data out of training runs into queryable storage, and the APIs, libraries, and services researchers use to manage and explore it. This role sits closer to the research workflow than a typical data infrastructure position: you'll often embed with research teams, build ML-specific tooling alongside them, and leverage what our Data Infrastructure team has already built rather than reinventing it. We do not require prior ML or AI training experience. If you enjoy working closely with technical users, learning new domains quickly, and building tools people actually want to use, you'll pick up the research context fast. Responsibilities • Build and operate data pipelines that extract data from research training runs and land it in storage systems that are easy and fast to query • Work closely with researchers to design and build APIs, libraries, and web interfaces that support data management, exploration, and analysis • Develop dataset management, data cataloging, and provenance tooling that researchers use in their day-to-day work • Embed with research teams to understand their workflows, identify high-leverage tooling opportunities, and ship solutions quickly • Collaborate with adjacent teams to build on existing systems rather than reinventing them You may be a good fit if you • Have significant software engineering experience, particularly building data-intensive applications or internal tooling • Enjoy working directly with users, gathering requirements iteratively, and shipping things that get adopted • Are results-oriented, with a bias towards flexibility and impact • Pick up slack, even if it goes outside your job description • Want to learn more about machine learning research • Care about the societal impacts of your work

No apliques sin prepararte

Investigamos quién te entrevista, adaptamos tu CV y te ensayamos en vivo — gratis la primera.