[DH] Engineering Manager, AI Observability

Anthropic·San Francisco, CAlead

En corto

→Liderar un equipo de ingenieros que construye herramientas para supervisar y analizar datos de IA a gran escala.
→Día a día: diseñar sistemas de monitoreo con IA, colaborar con equipos de seguridad y investigación, y desarrollar integraciones autónomas con modelos.
→Destacado: las herramientas que se crean se usan directamente por decenas de investigadores y afectan la seguridad y alineación de los modelos de IA.

No se requiere inglés explícitamente, pero el puesto implica trabajo con equipos internaci

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¿Qué piden?

✓Al menos 2 años de experiencia como manager con interés activo en liderar personas
✓Más de 5 años de experiencia en ingeniería de software con exposición real a sistemas de ML
✓Interés en escalar la supervisión humana de sistemas de IA
✓Experiencia conocida en desarrollo de aplicaciones de LLM (ingeniería de contexto, evaluación, orquestación)
✓Capacidad para cambiar entre trabajo de infraestructura profunda y pensamiento de producto
✓Habilidad para trabajar en entornos colaborativos y multifuncionales

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

LLMAI monitoring systemsunstructured text processingagentic integrationsmodel auditsthreat intelligencecontext engineeringevaluationorchestrationresearch collaboration

¿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 Team As AI training and deployments scale, the volume of data we need to monitor and understand is exploding. Our team uses Claude itself to make sense of this data. We own an integrated set of tools enabling Anthropic to ask open-ended questions, surface unexpected patterns, and maintain meaningful human oversight over massive datasets. Our tools are widely adopted internally — powering ongoing enforcement , threat intelligence investigations , model audits , and more — and we're looking for an experienced engineering manager to help us both scale up existing applications and go zero-to-one on new ones. About the Role As an Engineering Manager for the team, you'll lead research engineers who design and build systems that let AI analyze large, unstructured datasets — think tens or hundreds of thousands of conversations or documents — and produce structured, trustworthy insights. The team works across the full stack, from core analysis frameworks through user-facing apps and interfaces. This is a high-leverage role. The tools you build will be used by dozens of researchers and investigators, and directly shape our ability to measure and mitigate both misuse and misalignment. Responsibilities: • Lead the design and implementation of AI-based monitoring systems for AI training and deployment • Extend and improve core frameworks for processing large volumes of unstructured text • Partner with researchers and safety teams across Anthropic to understand their analytical needs, and prioritize the team's work to build solutions • Develop agentic integrations that allow AI systems to autonomously investigate and act on analytical findings • Contribute to the strategic direction of the team, including decisions about what to build, what to partner on, and where to invest • Coach and support your reports to understand and pursue their professional growth • Run the team's recruiting efforts, ensuring we can grow as quickly as we need • Design processes that help the team operate effectively You May Be a Good Fit If You: • Are an experienced manager (at least 2 years) and actively enjoy people management • Have 5+ years of software engineering experience, with meaningful exposure to ML systems • Are excited about the problem of scaling human oversight of AI systems • Are familiar with LLM application development (context engineering, evaluation, orchestration) • Enjoy building tools that other people use — you care about UX, reliability, and documentation • Can context-switch between deep infrastructure work and user-facing product thinking • Thrive in collaborative, cross-functional environments Strong Candidates May Also Have: • Research experience in AI safety, alignment, or responsible deployment • Strong people management experience: coaching, performance evaluation, mentorship, career development • E

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