Software Engineer, Applied AI Infrastructure

Nuro · Mountain View, California (HQ)mid

En corto

▸Construyes infraestructura de IA autónoma para que agentes confiables ejecuten tareas de ingeniería sin supervisión.
▸Te enfocas en evaluación cerrada, seguridad de agentes y automatización de investigación técnica.
▸El equipo opera con autonomía directa con el CEO y lidera un sistema de IA que debe ser más riguroso que cualquier otro en el mundo.

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¿Qué piden?

✓Experiencia en desarrollo de sistemas de evaluación de IA con retroalimentación cerrada.
✓Conocimiento en orquestación y aislamiento de agentes autónomos.
✓Habilidades en diseño de plataformas seguras para ejecución de código en producción.
✓Capacidad para construir sistemas de observabilidad y auditoría para agentes.
✓Experiencia con automatización de tareas de investigación técnica.
✓Compromiso con la rigurosidad científica y la medición estadística en IA.

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

PythonGoDockerKubernetesPostgreSQLRedisPrometheusGrafanaGitCI/CD

¿A quién escribirle en Nuro?

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Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors. About the Team Frontier models are fungible. Any team can rent the same intelligence we can, and the model we build on today will be replaced within a month. What is not fungible is the infrastructure that decides whether an autonomous system's output can be trusted — evaluation, verification, and the discipline to gate on evidence instead of impressions. Nuro has spent a decade building exactly that discipline for a robot that drives on public roads, and this team turns it inward: we build the platform that lets AI agents operate autonomously inside Nuro's own engineering organization, under the same standard of proof we apply to the vehicle. Our mandate is to amplify the output of every engineer and researcher at Nuro by 100x. Not a better IDE, not a faster build — a change in what a single person can attempt. That number is a target, not a claim, and reaching it depends on one thing above all: autonomous work has to be trustworthy enough to run unattended. So our central ambition is to build the most rigorous closed-loop evaluation system for AI work anywhere. Leverage follows from trust, and trust follows from measurement. We operate as a startup inside a company that has already shipped a hard thing. Small team, no established playbook, direct access to compute and to the systems we are automating. You will work directly with engineering leadership and the CEO, and the decisions you make will be yours to make rather than yours to implement. About the Role We already operate a substantial agent system in production — a fleet of agents with an extensive library of skills and plugins, integrated into the tools our engineers use daily. This role is about what it takes to make that system trustworthy, autonomous, and an order of magnitude more capable. Three things sit at the center of it. Closed-loop evaluation. Our ambition is to build the most rigorous evaluation system for AI work anywhere — closed-loop, meaning every agent action produces a measurable outcome that feeds back into whether that agent is trusted to act again. Acceptance, revert, and override rates per workflow. Statistical honesty about whether a difference is real. Regression detection that fires before a human notices. Everything else on this team depends on this being right, and almost nobody has built it well. Agent platform. The runtime that makes autonomous agents safe to run against real systems: orchestration, sandboxing and isolation, tool and skill frameworks, memory, identity and permissioning, and the gateway and observability layer underneath. Agents that touch production code and production infrastructure need containment and auditability before they need capability. Autoresearch infrastructure. The automation of the resear

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