Machine Learning Infrastructure Engineer (Platform)

Join our Infrastructure team as a Machine Learning Infrastructure Engineer. You will be responsible for building and operating the backbone of our AI-native infrastructure, ensuring scalability from prototype to production-grade deployments. Your role will involve driving security, observability, and cost-aware infrastructure, improving developer experience, and collaborating closely with researchers and other engineers. You should have experience in cloud platforms, distributed systems, Kubernetes, and infrastructure-as-code.

• Operar y evolucionar clústeres de Kubernetes y patrones de implementación de servicios, y ayudar a construir una plataforma de microservicios escalable para sistemas internos.
• Tratar la seguridad, el registro, las métricas, el seguimiento y la alerta como primitivos de plataforma de primera clase, y construir sistemas que resalten los problemas de confiabilidad y rendimiento.
• Colaborar y liderar a través de la propiedad, trabajando en estrecha colaboración con investigadores y otros ingenieros para comprender los requisitos y las limitaciones.

• Ability to reason about system bottlenecks, performance tuning, and cost optimizations across compute, networking, and storage
• Experience (4-6 years) working in fast-moving or early-stage environments where ambiguity is normal with demonstrated growth trajectory
• Solid intuition around scalability, performance, and failure modes
• Deep experience with cloud platforms (GCP, AWS) and distributed systems: compute orchestration, networking, autoscaling, service meshes, load balancing
• Comfort with Kubernetes, cluster-level reliability, and service-oriented architectures
• Experience with infrastructure-as-code (e.g. Terraform), containerization, and modern platform engineering practices
• Familiarity with agent infrastructure, observability, security and sandboxing
• Extremely strong first-principles thinking
• Strong cross-functional communication and ownership mindset
• Familiarity with logging, metrics, tracing, incident response, SLOs, and debugging complex distributed systems
• Ultimately, we’re looking for someone who can spin up quickly on unfamiliar and ambiguous domains, has a strong sense of ownership, and cares deeply about our mission. Even if you don’t check all the boxes above, we strongly encourage you to apply if this sounds like you
• Experience with large-scale ML training, evaluation, or simulation infrastructure
• Experience with secrets management systems (e.g., Doppler)
• Exposure to robotics, simulation, or real-time systems
• Background in observability, cost optimization, or internal platform tooling