Senior Infrastructure Engineer at Quantum Sky

Job description

Quantum Sky is seeking a Senior Infrastructure Engineer to design, build, and operate the cloud and AI infrastructure behind Project 137, the company's applied-research and IP engine. Reporting to the Director of Infrastructure Engineering, this engineer will own Google Cloud Platform (GCP) and Kubernetes environments that run AI/ML training, inference, and data workloads. The role is approximately 85% hands-on engineering and 15% project coordination. The ideal candidate is a senior practitioner with deep GCP expertise, production experience supporting AI workloads, and the organizational discipline to plan and schedule infrastructure work across teams. Responsibilities: Cloud and AI infrastructure • Design, build, and operate production infrastructure on Google Cloud Platform, including landing zones, VPC networking, Cloud IAM, storage, and organization policy. • Engineer and scale infrastructure for AI/ML workloads: GPU and TPU capacity, distributed training, model serving and inference, and high-throughput data pipelines. • Support data-science and research teams on Vertex AI, Kubeflow, and related MLOps tooling, including environment provisioning, model registries, and experiment infrastructure. • Build and maintain hybrid connectivity between on-premises data-center and cloud environments, including Cloud Interconnect, VPN, DNS, and load balancing. Kubernetes and platform engineering • Architect and operate GKE clusters, including GPU node pools, autoscaling, workload identity, networking and ingress, and version upgrades. • Build reusable platform components and Helm charts that give engineering and research teams self-service access to compute and AI tooling. • Define and automate infrastructure with Terraform and GitOps practices, using Git as the source of truth for configuration, manifests, and CI/CD pipelines. Reliability and security • Implement observability, alerting, and SLOs using Cloud Operations, Prometheus, and Grafana; participate in on-call and lead root-cause analysis on incidents. • Apply cloud security controls: least-privilege IAM, secrets management, container image scanning, policy-as-code, and software supply-chain protections. • Plan, build, and test backup and disaster-recovery capabilities for platform and AI workloads. • Mentor engineers, lead design reviews, and document architecture standards and runbooks. Project coordination (approx. 15%) • Plan and schedule infrastructure workstreams, maintaining timelines, milestones, and dependency tracking across engineering, research, and operations teams. • Organize technical work into clear tasks and priorities, and report progress and blockers to the Director of Infrastructure Engineering. Required: • Education: Bachelor's degree in computer science, engineering, or a related technical field. Equivalent experience considered. • Experience: 5+ years in infrastructure, platform, cloud, or site reliability engineering, including 2+ years of hands-on production work on Google Cloud Platform. • Google Cloud: Deep expertise across Compute Engine, GKE, VPC, Cloud IAM, Cloud Storage, Cloud Operations. • AI infrastructure: Experience supporting AI/ML workloads in production, including GPU or TPU provisioning, training and inference platforms, and MLOps tooling such as Vertex AI or Kubeflow. • Kubernetes: Production experience operating Kubernetes, including cluster operations, Helm, networking and ingress, RBAC, autoscaling, and upgrades. • Automation: Strong Terraform and Git-based workflow skills; experience with CI/CD tooling such as Cloud Build, GitHub Actions, GitLab CI, or Argo CD. • Scripting: Proficiency in Python, Go, or Bash for automation and tooling. • Networking and systems: Solid understanding of Linux, TCP/IP, DNS, load balancing, and hybrid cloud connectivity. • Organization: Proven ability to plan, schedule, and track technical work across multiple concurrent workstreams using tools such as Jira or Confluence. • Communication: Clear written and verbal skills; able to explain technical trade-offs to engineers, researchers, and leadership. Desired: • Google Cloud Professional Cloud Architect, Professional Cloud DevOps Engineer, or Professional Machine Learning Engineer certification. • Certified Kubernetes Administrator (CKA), Application Developer (CKAD), or Security Specialist (CKS). • Experience with GPU scheduling and sharing on Kubernetes (NVIDIA GPU Operator, Kueue, or Ray) and large-scale distributed training. • Familiarity with data-center fundamentals for high-density compute, including power, cooling, and capacity planning. • Multi-cloud experience with AWS or Azure alongside GCP. Location: • This position is based in [Reston, VA] with a [hybrid / on-site] schedule. Occasional travel of approximately 10-20% to partner sites and data-center locations. Compensation: • Compensation is unique to each candidate and relative to the skills and experience they bring to the position. The salary range for this position is typically between $175,000-$210,000. This does not guarantee a specific salary as compensation is based upon multiple factors such as education, experience, certifications, and other requirements, and may fall outside of the above-stated range. Benefits: • Highlights of our benefits include Health/Dental/Vision, 401(k) match, Paid Time Off, STD/LTD/Life Insurance, Referral Bonuses, professional development reimbursement, and parental leave. The world the mission operates in is going post-quantum, contested, and machine-speed. Quantum Sky engineers the advantage across cyber, networks, software, and quantum because the mission demands dominance, not parity. We don't follow the map. We draw it. At Quantum Sky, we believe that success starts with our people. We foster a collaborative, innovative, and mission-driven environment where every team member plays a critical role in shaping the future of technology. Are you ready to join #TeamQuantumSky? Quantum Sky Engineering LLC is an Equal Opportunity Employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, [sexual orientation, gender identity,] national origin, disability, status as a protected veteran, or any characteristic protected by applicable law.

Requirements

Must-haves

Cloud and AI infrastructure
Design, build, and operate production infrastructure on Google Cloud Platform, including landing zones, VPC networking, Cloud IAM, storage, and organization policy.
Engineer and scale infrastructure for AI/ML workloads: GPU and TPU capacity, distributed training, model serving and inference, and high-throughput data pipelines.
Support data-science and research teams on Vertex AI, Kubeflow, and related MLOps tooling, including environment provisioning, model registries, and experiment infrastructure.
Build and maintain hybrid connectivity between on-premises data-center and cloud environments, including Cloud Interconnect, VPN, DNS, and load balancing.
Kubernetes and platform engineering
Architect and operate GKE clusters, including GPU node pools, autoscaling, workload identity, networking and ingress, and version upgrades.
Build reusable platform components and Helm charts that give engineering and research teams self-service access to compute and AI tooling.
Define and automate infrastructure with Terraform and GitOps practices, using Git as the source of truth for configuration, manifests, and CI/CD pipelines.
Reliability and security
Implement observability, alerting, and SLOs using Cloud Operations, Prometheus, and Grafana; participate in on-call and lead root-cause analysis on incidents.
Apply cloud security controls: least-privilege IAM, secrets management, container image scanning, policy-as-code, and software supply-chain protections.
Plan, build, and test backup and disaster-recovery capabilities for platform and AI workloads.
Mentor engineers, lead design reviews, and document architecture standards and runbooks.
Project coordination (approx. 15%)
Plan and schedule infrastructure workstreams, maintaining timelines, milestones, and dependency tracking across engineering, research, and operations teams.
Organize technical work into clear tasks and priorities, and report progress and blockers to the Director of Infrastructure Engineering.
Education: Bachelor's degree in computer science, engineering, or a related technical field. Equivalent experience considered.
Experience: 5+ years in infrastructure, platform, cloud, or site reliability engineering, including 2+ years of hands-on production work on Google Cloud Platform.

Nice-to-haves

Google Cloud Professional Cloud Architect, Professional Cloud DevOps Engineer, or Professional Machine Learning Engineer certification.
Certified Kubernetes Administrator (CKA), Application Developer (CKAD), or Security Specialist (CKS).
Experience with GPU scheduling and sharing on Kubernetes (NVIDIA GPU Operator, Kueue, or Ray) and large-scale distributed training.
Familiarity with data-center fundamentals for high-density compute, including power, cooling, and capacity planning.
Multi-cloud experience with AWS or Azure alongside GCP.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aigcpkubernetesmlci/cdpythongojiramachine learningawsazure

Platform: icims
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Paid Time Off

Parental Leave

Professional Development