Sr Staff Site Reliability Engineer, AI Infrastructure

d-Matrix's SRE team owns the infrastructure layer that every engineering team and customer depends on — colocation facilities, on-premises GPU clusters, cloud environments, and the platform services used to deploy and validate d-Matrix hardware and software. This role is a core member of that team, responsible for reliability, automation, and observability across colo, on-premises lab, and cloud environments. You will own systems end-to-end, from provisioning through live incident response, partnering with hardware and software teams on CI/CD, QA, and HPC workloads for silicon development, as well as supporting customer-facing environments where d-Matrix partners collaborate on deployments.

This is hands-on, high-ownership work: you'll build and operate real infrastructure, not manage tickets.

• Own reliability and availability across colo server fleets, on-premises lab clusters, cloud environments (AWS, Azure, GCP), and customer-facing platform services.
• Perform hands-on infrastructure work — server provisioning, OS configuration, networking, storage, and hardware troubleshooting — from bare metal through auto-scaling Kubernetes environments.
• Lead capacity planning and hardware lifecycle management for your domains, and track cloud spend to support FinOps and workload placement decisions.
• Drive all provisioning, deployment, and operational changes through Terraform and/or Ansible rather than manual steps, and contribute to shared IaC modules used across the global SRE and data center services teams.
• Build and document automation that eliminates toil — host lifecycle management, fleet health checks, auto-remediation, self-service tooling, and networking automation for cluster interconnects and lab configurations.
• Design and maintain monitoring, alerting, and SLIs (Prometheus/Grafana, DataDog, Splunk, or equivalent), contributing to AIOps-driven detection workflows.
• Participate in on-call rotation, triaging and resolving incidents from bare metal to application layer, and produce high-quality RCAs for P0/P1 incidents.
• Support platform services used by internal teams and external customers, ensuring QoS and uptime commitments and documenting operational runbooks.

• Bachelor's or Master's in Computer Science, Electrical Engineering, or a related field (or equivalent experience); 7+ years in SRE, infrastructure engineering, or systems administration.
• Strong Linux systems knowledge with hands-on colocation or on-premises server infrastructure — networking, storage, systemd, kernel parameters, performance diagnostics, physical hardware, rack networking, and bare-metal provisioning.
• Production IaC experience with Terraform and/or Ansible — writing and maintaining configurations, not just running existing playbooks.
• Kubernetes operational experience: cluster troubleshooting, workload management, storage, and networking.
• Experience with observability tooling – Prometheus/Grafana, DataDog, Splunk, or equivalent — including building dashboards and writing alert rules.
• Production-quality Python and/or Bash scripting, paired with incident response experience: structured triage, RCA production, and follow-through on action items.

• Experience operating customer-facing infrastructure or platform services with external reliability expectations.
• Cloud infrastructure operations across AWS, Azure, or GCP, including hybrid environments spanning cloud and on-prem.
• Experience deploying and operating AI-driven infrastructure tools — AIOps platforms, intelligent alerting, anomaly detection, or LLM-assisted diagnostics — in production.
• HPC job scheduler experience: Slurm, LSF, or equivalent.
• Knowledge of high-speed interconnect fabrics: InfiniBand, RoCE, or NVLink.
• Experience with large-scale infrastructure automation — host lifecycle management, fleet auto-healing, or AIOps-driven operations — building tooling that reduces manual intervention, not just running it.

Join d-Matrix and help build the future of AI inference. We’ve pioneered a memory-centric architecture that brings compute directly into memory, making AI faster, more energy efficient, and accessible on a global scale. From chips and software to rack-scale systems, our team is tackling some of AI infrastructure’s hardest challenges — and shipping breakthrough technology today. If you want to create what comes next and help make infinite inference possible, we’d love to hear from you.

Our culture is built on respect, collaboration, and direct communication. We value humility, we believe our different perspectives lead to better solutions, and we’re looking for people who are driven by execution and energized by hard problems.

Role Overview