Senior DevOps Engineer

<p>Humanoid is the first AI and robotics company in the UK, creating the world’s most advanced, reliable, commercially scalable, and safe humanoid robots. Our first humanoid robot HMND 01 is a next-gen labour automation unit, providing highly efficient services across various use cases, starting with industrial applications.</p><p><br><br>Want to apply Read all the information about this position below, then hit the apply button.<br></p><p><strong>Our Mission</strong></p><p>At Humanoid we strive to create the world’s leading, commercially scalable, safe, and advanced humanoid robots that seamlessly integrate into daily life and amplify human capacity.</p><p><br></p><p>We are building large-scale compute infrastructure for training next-generation robotics models, including transformer-based systems like VLA. </p><p>This role focuses on designing and operating multi-GPU, cross-cloud platforms that enable efficient, reliable, and scalable model training. You’ll work at the intersection of DevOps, MLOps, and distributed systems, helping push the limits of real-world AI.</p><p><br></p><p><strong>What You’ll Do: </strong></p><ul><li>Design, build, and operate scalable multi-GPU infrastructure across cloud environments (AWS, GCP, etc.)</li><li>Own the reliability, performance, and cost-efficiency of model training platforms</li><li>Develop and maintain infrastructure-as-code and automation for provisioning, orchestration, and lifecycle management</li><li>Build and evolve CI/CD pipelines for both infrastructure and ML training workflows</li><li>Optimize distributed training workloads (scheduling, resource utilization, observability)</li><li>Ensure high standards of reliability, scalability, security, and monitoring across systems</li><li>Collaborate with ML engineers and researchers to enable efficient experimentation and productionization</li><li>Troubleshoot complex issues across distributed systems, networking, and GPU workloads</li><li>Define and implement best practices in DevOps/MLOps for a fast-scaling environment</li><li>Document systems, architecture decisions, and operational processes</li></ul><p><br></p><p><br></p><p><strong>We’re Looking For:</strong></p><ul><li>5+ years of experience in DevOps, MLOps, or infrastructure engineering (Senior/Staff level)</li><li>Strong experience with Kubernetes and containerized workloads at scale</li><li>Proven experience with Infrastructure-as-Code (Terraform, Helm, or similar)</li><li>Deep familiarity with at least one major cloud provider (AWS preferred)</li><li>Solid experience building CI/CD systems (e.g., GitHub Actions, GitLab CI, ArgoCD)</li><li>Proficiency in Python for automation and tooling</li><li>Strong understanding of distributed systems, networking, and system reliability</li><li>Ability to operate independently and drive large infrastructure initiatives</li></ul><p>Nice to have: </p><ul><li>Hands-on experience with multi-GPU and/or distributed compute environments</li><li>Experience with GPU scheduling/orchestration (e.g., xwzovoh Kubernetes schedulers - Volcano, Ray, etc.)</li><li>Experience supporting ML workloads or training pipelines (PyTorch, TensorFlow, etc.)</li><li>Experience with multi-cloud or hybrid cloud environments</li><li>Background in performance optimization for training workloads</li><li>Experience in robotics, simulation, or embodied AI systems</li></ul><p><br></p><p><br></p><p><strong>What we offer: </strong></p><ul><li>Competitive salary plus participation in our Stock Option Plan</li><li>Paid vacation with adjustments based on your location to comply with local labor laws</li><li>Travel opportunities to our Vancouver and Boston offices</li><li>Office perks: free breakfasts, lunches, snacks, and regular team events</li><li>Freedom to influence the product and own key initiatives</li><li>Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics</li><li>Startup culture prioritising speed, transparency, and minimal bureaucracy</li></ul>