Software Engineer, Infrastructure, Interpretability

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

En corto

▸Construir infraestructura de seguridad y escalabilidad para investigar modelos de IA avanzados.
▸Trabajar de cerca con científicos de interpretabilidad para eliminar fricciones en su trabajo diario.
▸Ser un early hire en un equipo clave para la seguridad y transparencia de la IA de vanguardia.

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?

✓Experiencia en ingeniería de infraestructura de software
✓Conocimiento de sistemas distribuidos y arquitectura de alto rendimiento
✓Habilidades en diseño de entornos seguros y privados
✓Capacidad para colaborar con equipos de investigación y seguridad
✓Experiencia con gestión de datos a gran escala y recursos de cómputo
✓Enfoque centrado en la experiencia del desarrollador y herramientas de observabilidad

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

PythonGoBazelTerraformKubernetesDockergRPCPrometheusOpenTelemetryPostgreSQL

¿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: When you see what modern language models are capable of, do you wonder, "How do these things work? How can we trust them?" The Interpretability team at Anthropic works to understand what's actually happening inside trained models - and applies our best techniques to keep frontier AI safe as it rapidly improves. Think of us as doing "neuroscience" of neural networks using "microscopes" we build - or reverse-engineering neural networks like binary programs. More resources to learn about our work: • Our Research blog - covering advances including Monosemantic Features and Circuits • An Intro to Interpretability from our research lead, Chris Olah • The Urgency of Interpretability from CEO Dario Amodei • Engineering Challenges Scaling Interpretability - directly relevant to this role • 60 Minutes segment - see a demo of tooling our team built • New Yorker article - what it's like to work on one of AI's hardest open problems This role is an early hire on a new infrastructure effort within Interpretability: you'll help define its charter, not just execute it. Interpretability research requires deep access to frontier models while retaining a high degree of research flexibility. Your job is to build the paved path that makes that access secure by default, private by design, and low-friction for every researcher. The work spans four areas: • Security : design the secure-by-default environments and access patterns that enable deep model access for an organization whose research requires it - done well, the same design improves both our security posture and research productivity. • Privacy : build data-access patterns that ensure policy adherence as our research moves from theory into practical application • Data & Compute Management : manage research data at petabyte scale and make efficient use of large accelerator fleets - storage lifecycle, capacity planning, and scheduling. • Developer experience : agentic engineering, tooling and observability that keep researchers moving fast In this role, you’ll be deeply embedded alongside Interp Researchers to understand their workflows - building your understanding of the research as you go; at the same time you’ll bridge communication with Anthropic’s wider platform and security teams.. Every hour of researcher friction you remove is multiplied across the whole organization, and the infrastructure you build sets the pace at which interpretability results reach real safety decisions.

No apliques sin prepararte

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