We are dedicated to creating the world’s most advanced, reliable, and commercially scalable humanoid robots. Our flagship next‑gen labor automation units are designed to seamlessly integrate into daily life and amplify human capacity, starting with high‑efficiency industrial applications.

To support this mission, we are building massive‑scale compute infrastructure to train next‑generation robotics models, including transformer‑based systems like VLA. As a

Staff Engineer , you will sit at the intersection of DevOps, MLOps, and distributed systems. You will lead the design, evolution, and reliability of a multi‑GPU, cross‑cloud platform that enables cutting‑edge AI to function in real‑world environments.

Responsibilities

Architecture & Leadership: Lead the design, evolution, and long‑term technical direction of scalable, multi‑GPU infrastructure and model training platforms across cloud environments (AWS, GCP, etc.).

Scale & Optimization: Drive reliability, performance, and cost‑efficiency at scale; optimize distributed training workloads (scheduling, resource utilization, observability).

Automation & CI/CD: Build and evolve infrastructure‑as‑code and automation for provisioning, orchestration, and lifecycle management. Architect and improve CI/CD systems for both infrastructure and ML training workflows.

Collaboration: Partner closely with ML engineers and researchers to enable efficient experimentation and seamless productionisation.

SRE & Troubleshooting: Lead the troubleshooting and resolution of complex system issues across distributed, GPU‑heavy environments.

Best Practices & Mentorship: Define and implement best practices for infrastructure, DevOps, and MLOps across the organization. Mentor engineers and raise the bar for overall engineering quality and operational excellence.

Documentation: Document architecture, systems, and key technical decisions clearly.

Requirements

Production‑grade, hands‑on experience with Kubernetes (CKA certification is preferred).





Production‑grade, hands‑on experience using Terraform.

Experience with Kubernetes application packaging and release management using Helm.

Hands‑on experience operating heavy workloads on a major cloud provider (specifically AWS).

Experience building and operating CI/CD pipelines, including self‑hosted build runners (GitHub Actions).

Deep hands‑on experience with monitoring and alerting stacks (Prometheus and Grafana).

Strong foundational knowledge in Linux administration, containerisation, and container orchestration.

Solid automation and scripting skills utilizing Python and Bash.

Flexibility to participate in an on‑call rota for urgent issues outside of regular business hours.

Nice to have

Hands‑on experience operating GPU‑accelerated Kubernetes clusters (NVIDIA).

Experience with gang scheduling, resource allocation, and fair‑sharing (queues and priorities) for large‑scale ML training.

Experience with cluster autoscaling/dynamic node provisioning (Karpenter) and high‑performance shared storage systems (FSx for Lustre, EFS).

We offer

Opportunity to work on bleeding‑edge projects

Work with a highly motivated and dedicated team

Competitive salary

Versátil schedule

Benefits package – medical insurance, sports

Corporate social events

Professional development opportunities

Well‑equipped office

About Us Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation.

A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.

📌 Staff DevOps & MLOps Engineer (Madrid)
🏢 Grid Dynamics
📍 Madrid