FluidStack

1 week ago

Compute Deployment Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 3 hours agoStatus: Live

Job Summary

Own compute turn-up from facility availability through ready-for-service, qualifying racks at scale across hundreds of GPU/custom-accelerator nodes. Drive qualification via a base-management Kubernetes platform and provisioning stack (discovery, imaging, firmware updates, shared services), automate hardware workflows, and methodically triage failures across hardware, firmware, and software. Run turn-ups remotely by default with on-site pulses when new data halls go live, partnering with network and data center operations during windows.

Location: San Francisco, New York

Workplace: Onsite

Employment Type: Full time

Job Function: DevOps, Cloud & Infrastructure

Key Responsibilities

•Own compute turn-up from facility availability to ready-for-service for new capacity.
•Qualify racks at scale by establishing firmware baselines, configuring BMC/BIOS, running burn-in, and validating node/cluster readiness on GPU/custom accelerator platforms.
•Drive qualification through the base-management Kubernetes platform and provisioning stack (discovery, imaging, firmware updates, shared services), using tooling to burn down qual queues.
•Triage hardware failures found in qualification by isolating to components, driving RMA/vendor escalation, and feeding failure patterns back into qualification gates.
•Run turn-ups remotely by default, with on-site pulses (about a week per data hall) and partner with network deployment, ICT, and data center operations during turn-up windows.

Travel: Medium travel

Pay and Benefits

Salary: USD 150,000 - 250,000 annually

Key Requirements

•Have brought up server or GPU fleets at scale (hundreds of nodes or more) and taken them all the way to production.
•Work deep in Linux and out-of-band management (BMC, IPMI, Redfish) as daily tools.
•Automate hardware workflows in Python or Go instead of manual clicking, turning repeat work into software.
•Be effective working physically in data halls (racking, cabling, swapping components) or directing remote hands.
•Triage failures methodically across hardware, firmware, and software by isolating faults to components before fixing.

Experience:AI infrastructureData centersGPU fleetsBare-metal provisioning

Skills:Extreme ownershipVelocityFirst principles thinkingCross-functional collaborationMethodical troubleshooting

Tech Stack:LinuxBMCIPMIRedfishPythonGoBIOSKubernetesBare-metal provisioningFirmware updatesNVIDIAAMDDCIMInventory toolingBurn-inStress testingRMA

Company Brief

FluidStack

Builds and deploys large-scale GPU cloud infrastructure for AI labs, enterprises and governments, providing high-performance AI training and inference capacity and rapid data-center deployment services.

Industry: Cloud Computing

Company Size: Medium (51 to 250 employees)

Growth: Growth Stage Startup

Funding: Series A

Headquarters: New York, United States

Founded: 2017

Glassdoor: 4.7

Website LinkedIn Glassdoor

Loading job details...

Preparing the role view and application actions.

FluidStack

1 week ago

Compute Deployment Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 3 hours agoStatus: Live

Job Summary

Location: San Francisco, New York

Workplace: Onsite

Employment Type: Full time

Job Function: DevOps, Cloud & Infrastructure

Key Responsibilities

•Own compute turn-up from facility availability to ready-for-service for new capacity.
•Qualify racks at scale by establishing firmware baselines, configuring BMC/BIOS, running burn-in, and validating node/cluster readiness on GPU/custom accelerator platforms.
•Drive qualification through the base-management Kubernetes platform and provisioning stack (discovery, imaging, firmware updates, shared services), using tooling to burn down qual queues.
•Triage hardware failures found in qualification by isolating to components, driving RMA/vendor escalation, and feeding failure patterns back into qualification gates.
•Run turn-ups remotely by default, with on-site pulses (about a week per data hall) and partner with network deployment, ICT, and data center operations during turn-up windows.

Travel: Medium travel

Pay and Benefits

Salary: USD 150,000 - 250,000 annually

Key Requirements

•Have brought up server or GPU fleets at scale (hundreds of nodes or more) and taken them all the way to production.
•Work deep in Linux and out-of-band management (BMC, IPMI, Redfish) as daily tools.
•Automate hardware workflows in Python or Go instead of manual clicking, turning repeat work into software.
•Be effective working physically in data halls (racking, cabling, swapping components) or directing remote hands.
•Triage failures methodically across hardware, firmware, and software by isolating faults to components before fixing.

Experience:AI infrastructureData centersGPU fleetsBare-metal provisioning

Skills:Extreme ownershipVelocityFirst principles thinkingCross-functional collaborationMethodical troubleshooting

Tech Stack:LinuxBMCIPMIRedfishPythonGoBIOSKubernetesBare-metal provisioningFirmware updatesNVIDIAAMDDCIMInventory toolingBurn-inStress testingRMA

Company Brief

FluidStack

Industry: Cloud Computing

Company Size: Medium (51 to 250 employees)

Growth: Growth Stage Startup

Funding: Series A

Headquarters: New York, United States

Founded: 2017

Glassdoor: 4.7

Website LinkedIn Glassdoor