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Intern Software Engineer Gpu Jobs in Missouri (NOW HIRING)

Jobgether

$83K - $113K/yr

... and platform software. The role sits at the intersection of datacenter operations, GPU ... Work with network engineering to validate switch configurations, routing, VLAN/VRF segmentation ...

Workiva

$40/hr

The Software Engineer Intern implements developer tools or product features on a rapid-release cycle. You will work in an agile development methodology to conduct code reviews and unit tests ...

Workiva

$40/hr

The Software Engineer Intern implements developer tools or product features on a rapid-release cycle. You will work in an agile development methodology to conduct code reviews and unit tests ...

Workiva

$40/hr

The Software Engineer Intern implements developer tools or product features on a rapid-release cycle. You will work in an agile development methodology to conduct code reviews and unit tests ...

Workiva

$40/hr

The Software Engineer Intern implements developer tools or product features on a rapid-release cycle. You will work in an agile development methodology to conduct code reviews and unit tests ...

Workiva

$40/hr

The Software Engineer Intern implements developer tools or product features on a rapid-release cycle. You will work in an agile development methodology to conduct code reviews and unit tests ...

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All JobsIntern Software Engineer Gpu JobsIntern Software Engineer Gpu Jobs in Missouri

Intern Software Engineer Gpu information

What is the difference between Intern Software Engineer Gpu vs Intern Software Engineer Cloud?

AspectIntern Software Engineer GpuIntern Software Engineer Cloud
Required CredentialsComputer Science degree or related, programming skills, familiarity with GPU programmingSimilar credentials, with emphasis on cloud platforms and networking
Work EnvironmentHardware-focused, GPU development labs, research teamsCloud infrastructure, remote teams, data centers
Employer & Industry UsageTech companies, hardware manufacturers, AI researchCloud service providers, SaaS companies, enterprise IT

Intern Software Engineer Gpu roles focus on GPU hardware and software development, often involving parallel computing and AI workloads. Intern Software Engineer Cloud positions emphasize cloud infrastructure, deployment, and scalability. Both roles require strong programming skills and industry knowledge but differ mainly in their technical focus and work environment.

What are the most commonly searched types of Software Engineer Gpu jobs in Missouri?

The most popular types of Software Engineer Gpu jobs in Missouri are:

What cities in Missouri are hiring for Intern Software Engineer Gpu jobs?

Cities in Missouri with the most Intern Software Engineer Gpu job openings:

Intern Software Engineer Gpu jobs near you

Senior Technical Operations & Deployment Engineer (GPU Cloud Infrastructure)

Jobgether

On-site

$83K - $113K/yr

Full-time

Posted 8 days ago

Key responsibilities

  • Coordinate and support datacenter deployment activities, including validation of physical infrastructure and readiness.

  • Support hardware commissioning, validation, and testing of GPU servers, storage, and network components.

  • Perform operational tasks such as incident management, validation of telemetry, and maintaining deployment documentation.

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Technical Operations & Deployment Engineer (GPU Cloud Infrastructure) based in Netherlands.

This is a highly hands-on infrastructure role focused on deploying, commissioning, and operating GPU cloud environments across regional and core datacenters.
You will turn validated architectures and bills of materials into production-ready infrastructure spanning hardware, networking, storage, Linux, and platform software.
The role sits at the intersection of datacenter operations, GPU infrastructure, network engineering, and cloud platform operations.
You will work with high-density NVIDIA GPU systems, advanced networking, storage platforms, Kubernetes, virtualization, and observability tooling.
As a practical technical escalation point, you will troubleshoot complex issues across physical and software layers and drive incidents through resolution.
You will also help establish deployment standards, validation procedures, documentation, and operational practices for a rapidly evolving AI infrastructure environment.
The role offers broad technical ownership in an international, fast-moving setting where hands-on execution and operational excellence are essential.

Accountabilities
  • Datacenter deployment: Coordinate deployments with datacenter providers, integrators, logistics teams, vendors, and internal engineering; validate rack layouts, power, cooling, airflow, cabling, labeling, and physical readiness.
  • Rack and infrastructure commissioning: Support rack-and-stack activities for GPU and CPU servers, storage, switches, routers, firewalls, PDUs, serial/OOB systems, and supporting infrastructure.
  • Cabling and connectivity: Validate fiber and copper cabling, optics, transceivers, breakout cables, port mappings, link speeds, redundancy, and management, storage, north-south, and east-west connectivity.
  • Hardware bring-up: Commission GPU servers, storage nodes, and platform infrastructure while validating BIOS, BMC, firmware, NICs, DPUs, GPUs, NVMe, RAID/HBA, PCIe topology, NUMA, thermals, power, and hardware health.
  • Hardware validation: Execute burn-in, stress, network, storage, and acceptance testing before production handover; troubleshoot issues involving GPUs, DPUs, NICs, optics, memory, disks, firmware, and BIOS.
  • Network deployment support: Work with network engineering to validate switch configurations, routing, VLAN/VRF segmentation, BGP, ECMP, EVPN/VXLAN, OVS/OVN, VyOS, firewalls, WAF infrastructure, and customer connectivity.
  • AI networking: Support validation of RoCE/RDMA fabrics for distributed AI workloads and troubleshoot issues such as link flaps, MTU mismatches, route errors, packet loss, PFC/ECN problems, and congestion.
  • Platform installation: Install and validate Ubuntu/Linux environments, NVIDIA drivers, CUDA, OFED or inbox drivers, Docker/containerd, KVM/QEMU, platform agents, and GPU infrastructure components.
  • Cloud and Kubernetes environments: Support CloudStack, Kubernetes, KubeVirt, GPU Operator, CSI/CNI integrations, GPU passthrough, SR-IOV, BlueField DPUs, VM networking, and container networking.
  • Storage integration: Support integration and validation of StorPool, Weka, local NVMe, and other supported storage platforms.
  • Operational readiness: Execute acceptance testing, produce deployment readiness reports, maintain runbooks, and ensure infrastructure is fully operational before customer or production handover.
  • Day-2 operations: Perform controlled firmware, OS, driver, BIOS, switch, and hardware maintenance while supporting production incidents and infrastructure escalations.
  • Incident management: Investigate operational failures, perform root-cause analysis, distinguish temporary workarounds from permanent fixes, and work with engineering to eliminate recurring issues.
  • Observability: Validate telemetry and monitoring across hosts, GPUs, DPUs, switches, storage, and platform components using tools such as Zabbix, Prometheus, Grafana, Loki, DCGM/NVML, and NVIDIA NetQ or equivalents.
  • Performance validation: Establish baselines for GPU, network, storage, and host performance and support benchmarking and infrastructure validation.
  • Documentation: Maintain accurate as-built records covering rack elevations, cable maps, port mappings, serial numbers, asset records, IP allocations, changes, and operational procedures.
  • Cross-functional coordination: Partner with infrastructure, networking, storage, platform, fleet automation, observability, product engineering, sales engineering, and service delivery teams.
  • Vendor management: Coordinate with datacenter providers, system integrators, server and storage vendors, NVIDIA, and networking suppliers to resolve deployment and infrastructure issues.
  • Continuous improvement: Feed field experience back into reference architectures, BOMs, rack designs, cabling standards, deployment playbooks, validation processes, and automation.
Requirements
  • Datacenter infrastructure: Strong hands-on experience deploying and maintaining datacenter infrastructure, ideally within GPU, HPC, AI cloud, private cloud, or high-density compute environments.
  • Bare-metal deployment: Proven ability to bring servers from physical installation and bare metal through validation and production readiness.
  • GPU infrastructure: Experience with NVIDIA GPU servers, drivers, firmware, PCIe topology, hardware validation, and high-performance compute environments.
  • Next-generation AI infrastructure: Familiarity with NVL72-style rack-scale architectures, NVLink/NVSwitch domains, in-rack networking, high-density power delivery, and OEM/NVIDIA validation requirements.
  • Datacenter readiness: Ability to assess power density, cooling, rack dimensions, floor loading, containment, serviceability, maintenance access, and other physical requirements for AI infrastructure.
  • Linux: Strong Linux troubleshooting capabilities and experience managing operating systems, kernels, drivers, and hardware interfaces.
  • Networking: Practical knowledge of VLANs, VRFs, BGP, ECMP, OVS/OVN, routing, OOB management, and high-speed datacenter connectivity.
  • GPU networking: Familiarity with NVIDIA/Mellanox networking, RoCE/RDMA, SR-IOV, BlueField DPUs, and high-performance east-west infrastructure.
  • Virtualization and containers: Experience with KVM/QEMU, VFIO, PCI passthrough, Docker/containerd, Kubernetes, and/or KubeVirt.
  • Storage: Experience integrating or troubleshooting local NVMe, storage nodes, and enterprise or distributed storage platforms.
  • Automation: Familiarity with Terraform, Ansible, Bash, and/or Python for deployment, validation, configuration, or operational automation.
  • Observability: Experience with infrastructure monitoring, telemetry, logs, metrics, health checks, and performance dashboards.
  • Documentation: Strong attention to detail and discipline in producing accurate as-built documentation, runbooks, validation records, and handover materials.
  • Troubleshooting: Strong systems-thinking ability across physical infrastructure, hardware, firmware, networking, Linux, storage, and platform layers.
  • Operational mindset: Comfortable supporting production environments, deployment windows, operational escalations, and customer-impacting incidents.
  • Communication: Able to clearly explain technical issues, risks, workarounds, and permanent solutions to engineering teams, vendors, and leadership.
  • Personal qualities: Highly practical, detail-oriented, calm under pressure, autonomous, and comfortable working both inside datacenters and remotely with smart-hands teams.
Benefits
  • Attractive compensation package reflecting your expertise, experience, transferable skills, and market conditions.
  • Full-time or contract engagement, depending on the agreed arrangement.
  • Europe-based remote working environment with flexibility.
  • Opportunity to work on cutting-edge GPU cloud and AI infrastructure at significant scale.
  • Hands-on exposure to NVIDIA GPU platforms, high-density datacenter environments, RoCE/RDMA networking, Kubernetes, virtualization, storage, and advanced observability.
  • Broad cross-functional scope spanning hardware, datacenter operations, networking, storage, Linux, and cloud platforms.
  • High-impact role within a fast-growing international scale-up.
  • Strong opportunities for technical growth and career development as the infrastructure platform expands.
  • Friendly, diverse, flexible, and international working environment.
  • Inclusive workplace committed to equal opportunity and respect for all qualified candidates.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
 Why Apply Through Jobgether? 
 
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
 
 
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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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