DevOps Engineer

The company creates advanced, reliable, and commercially scalable humanoid robots, focusing on next-gen labor automation units designed to integrate into daily life and amplify human capacity through high-efficiency industrial applications.

• Lead the design, evolution, and long-term technical direction of scalable, multi-GPU infrastructure and model training platforms across cloud environments;
• Drive reliability, performance, and cost-efficiency at scale, including optimization of distributed training workloads;
• 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;
• Partner with ML engineers and researchers to enable efficient experimentation and productionization;
• Lead the troubleshooting and resolution of complex system issues across distributed, GPU-heavy environments;
• Define and implement best practices for infrastructure, DevOps, and MLOps across the organization;
• Mentor engineers and raise the bar for engineering quality and operational excellence;
• Document architecture, systems, and key technical decisions.

• Production-grade, hands-on experience with Kubernetes;
• Production-grade, hands-on experience using Terraform;
• Experience with Kubernetes application packaging and release management using Helm;
• Hands-on experience operating heavy workloads on 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, containerization, 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: CKA certification, hands-on experience operating GPU-accelerated Kubernetes clusters (NVIDIA), experience with gang scheduling, resource allocation, and fair-sharing for large-scale ML training, experience with cluster autoscaling/dynamic node provisioning (Karpenter) and high-performance shared storage systems (FSx for Lustre, EFS).

• Competitive salary;
• Flexible schedule;
• Medical insurance;
• Sports benefits;
• Corporate social events;
• Professional development opportunities;
• Well-equipped office.