Infrastructure & IT
Managing internal technology systems, networks, devices, and tooling. Covers IT management, systems administration, network engineering, internal IT support/help desk, cloud infrastructure (internal), database administration, IAM, enterprise architecture, and SaaS management.
Roles
The canonical roles within Infrastructure & IT.
Business Applications Administrator
Administrators in this role configure, maintain, and optimize business-critical SaaS platforms—from HR systems like Workday and HiBob to financial platforms like NetSuite and Coupa, as well as collaboration tools and support systems. They spend their days troubleshooting user issues, managing system integrations, designing workflows that scale across global operations, and ensuring data accuracy and compliance as the company grows. What sets this role apart is the strategic ownership of entire system landscapes rather than single-tool support; these professionals act as trusted partners to finance, HR, and operations teams, translating complex business needs into system configurations while balancing tactical maintenance with roadmap planning. They typically sit within centralized IT or Operations teams in high-growth AI and enterprise software companies, where rapid scaling demands reliable, automated, and compliant systems infrastructure.
Infrastructure Engineer
Infrastructure Engineers at AI companies operate the physical and systems-level infrastructure the business depends on—servers, storage arrays, networking equipment, and the Unix/Linux environments hosted on them. The day-to-day is hands-on: diagnosing hardware and firmware faults, managing warranty replacements through vendors, performing root-cause analysis on systemic issues, and maintaining the operational health of data-center and corporate-IT hardware. Cloud and infrastructure-as-code work appears in many of these jobs, but the centre of gravity is closer to traditional systems administration and data-center operations than to cloud platform engineering. These engineers typically sit within IT, infrastructure operations, or data-center teams, partnering with networking, security, and application teams to keep infrastructure running as the business scales.
IT Leadership
IT Leadership roles at AI companies own the corporate technology function—identity systems, endpoints, collaboration tools, networking, SaaS governance, and the support model behind them—along with the team that runs it. The day-to-day is classical IT leadership: hiring and developing IT operations teams, setting strategy and budget, managing vendor and SaaS portfolios, owning enterprise security and compliance posture, and scaling the support model as the company grows. Some teams are pushing further toward automation-first operations and code-defined infrastructure, but it is a maturity dimension within the role rather than a defining differentiator across the population. These leaders typically report into a VP of Operations, CFO, or COO depending on company stage, partnering closely with security, networking, and engineering.
Systems Engineer
Systems Engineers in AI companies design and operate the enterprise technology platforms that enable researchers and product teams to work efficiently—managing identity systems like Okta, collaboration tools such as Google Workspace and Slack, and endpoint infrastructure while ensuring security and scalability. What distinguishes this role from general IT administration is the emphasis on automation-first problem solving: rather than simply maintaining systems, these engineers architect scalable workflows using APIs, infrastructure-as-code, and integration platforms to eliminate manual processes and reduce operational friction. They typically sit within IT Engineering or Enterprise Systems teams, partnering closely with Security and Infrastructure groups to support rapid company growth, and increasingly they're being asked to bridge traditional IT operations with emerging AI workflows and autonomous systems.
IT Support Specialist
IT Support Specialists at AI companies handle the day-to-day support load for distributed engineering and operations teams—hardware and software troubleshooting, account and access provisioning, onboarding and offboarding workflows, and the ticket queue that runs alongside it. The work is mainstream IT support: first-line and escalation troubleshooting across operating systems and devices, SaaS-application support, ticketing-system administration, and clear communication with users at varying technical levels. Some specialists pick up scripting or workflow-automation work to reduce repetitive load, but the core role is recognizable across any company that runs distributed knowledge work. These specialists typically sit within lean IT operations teams, partnering with security, people operations, and infrastructure teams on the workflows that connect them.
Security Infrastructure Engineer
This role designs, builds, and operates identity and access management systems that scale across cloud infrastructure, SaaS platforms, and internal services at AI companies. Engineers here balance automation with compliance, implementing SSO consolidation, RBAC models, and lifecycle management while reducing access sprawl and supporting rapid business growth. They work at the intersection of security governance and operational efficiency, partnering with infrastructure, IT, and compliance teams to embed least-privilege access into AI development workflows and multi-cloud environments. The role sits within security or infrastructure teams and demands expertise in identity platforms like Okta, cloud IAM services, and scripting automation to protect critical assets while enabling researchers and engineers to move quickly.
Data Center IT Technician
This role involves hands-on troubleshooting and maintenance of high-performance GPU infrastructure and server hardware in AI-scale data centers. Technicians diagnose and resolve complex hardware incidents, manage fiber and network connectivity, and ensure continuous uptime of critical systems supporting large-scale AI model training and inference workloads. They work in shift-based operations within distributed data center teams, collaborating with L3 engineers and infrastructure specialists to optimize system reliability and reduce mean time to repair—directly impacting the performance of AI clusters that power customer applications.
Network Engineer
Network Engineers at AI companies design, deploy, and operate the corporate and infrastructure networks the business runs on—wired and wireless LAN, WAN connectivity between sites, VPN and remote access, network security, and the automation that keeps it all maintainable. The day-to-day is classical enterprise networking: configuring and troubleshooting switching and routing, designing for high availability and disaster recovery, implementing zero-trust and segmentation patterns, and using infrastructure-as-code tooling to manage configurations at scale. A subset of these jobs—at companies running large-scale ML training infrastructure—does extend into specialized GPU-fabric and HPC networking (RoCE, InfiniBand, collective communication), but that is a specialization within the role rather than the canonical scope. These engineers typically sit within IT, infrastructure, or platform networking teams, partnering with security, infrastructure, and operations counterparts.
Recent Jobs
The latest Infrastructure & IT openings across the AI industry.