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d-Matrix

Delivers memory-integrated AI compute platforms

Senior Staff Site Reliability Engineer - AI Infrastructure

Site Reliability EngineeringConfirmed live in the last 24 hours
Full-timeSanta Clara, CA, USA$175k - $265k /yrHybrid

About the Job

Requirements

  • A Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field, or equivalent experience, and at least 7 years in site reliability engineering, infrastructure engineering, or systems administration.
  • Strong Linux systems knowledge and hands-on colocation or on-premises server infrastructure experience, including networking, storage, systemd, kernel parameters, performance diagnostics, physical hardware, rack networking, and bare-metal provisioning.
  • Production infrastructure-as-code experience with Terraform and/or Ansible, including writing and maintaining configurations rather than only running existing playbooks.
  • Kubernetes operational experience, including cluster troubleshooting, workload management, storage, and networking.
  • Experience with observability tooling such as 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, including structured triage, root-cause analysis production, and follow-through on action items.

Responsibilities

  • Own reliability and availability across colocation server fleets, on-premises lab clusters, cloud environments (AWS, Azure, GCP), and customer-facing platform services.
  • Perform hands-on infrastructure work including server provisioning, operating system configuration, networking, storage, and hardware troubleshooting, from bare metal through auto-scaling Kubernetes environments.
  • Lead capacity planning and hardware lifecycle management for assigned domains, and track cloud spend to support FinOps and workload-placement decisions.
  • Drive provisioning, deployment, and operational changes through Terraform and/or Ansible rather than manual steps, and contribute to shared infrastructure-as-code modules used across global site reliability engineering and data center services teams.
  • Build and document automation to eliminate toil, including 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 service-level indicators using Prometheus/Grafana, DataDog, Splunk, or equivalent, contributing to AIOps-driven detection workflows.
  • Participate in an on-call rotation, triage and resolve incidents from bare metal to the application layer, and produce root-cause analyses for P0/P1 incidents.
  • Support platform services used by internal teams and external customers, ensuring quality-of-service and uptime commitments and documenting operational runbooks.

Desired Qualifications

  • 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-premises.
  • Experience deploying and operating AI-driven infrastructure tools in production, such as AIOps platforms, intelligent alerting, anomaly detection, or large language model-assisted diagnostics.
  • High-performance computing job scheduler experience with Slurm, LSF, or equivalent.
  • Knowledge of high-speed interconnect fabrics such as InfiniBand, RoCE, or NVLink.
  • Experience with large-scale infrastructure automation, including host lifecycle management, fleet auto-healing, or AIOps-driven operations, and building tooling that reduces manual intervention rather than merely running it.

About the Company

d-Matrix

Delivers memory-integrated AI compute platforms

d-Matrix provides scalable AI compute platforms for large datacenters with a focus on energy efficiency. Its core offering, the digital in-memory compute (DIMC) engine, places compute directly in programmable memory and uses a fabric of low-power, modular chiplets plus software to optimize AI inference. It differentiates itself by combining memory-integrated computation with a modular, chiplet-based design that reduces power use and data movement for high-volume workloads. The goal is to help large datacenters run AI workloads more efficiently at scale through customizable, energy-efficient compute solutions.

Company size
201-500
Company stage
Series C
Total funding
$429M
Headquarters
Santa Clara, CA
Founded
2019

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