AI Compute Engineer

BreakmarkHR

. Define lifecycle standards for NVIDIA HGX platforms and validated baselines for firmware, BIOS, BMC, OS, drivers, CUDA, and NCCL .

Posted 10/5/2026full-timeMedellín • ColombiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in NVIDIA GPU infrastructure, including HGX platforms, with a strong focus on burn-in and qualification processes for multi-node GPU clusters. Proficient in root-cause analysis, performance troubleshooting, and defining validation strategies across hardware and software domains.

Highest-signal resume keywords

NVIDIA GPU InfrastructureBurn-In And Qualification DesignLinux Systems AdministrationRoot-Cause AnalysisPerformance Troubleshooting

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

NVIDIA DriversCUDANCCLDCGMGPU Platform ToolingFleet-Level AnalysisFirmware BaselinesBIOS BaselinesOS BaselinesMulti-Node GPU Communication

Soft Skills

Clear CommunicationProblem-SolvingAdaptability

Tools & Technologies

NVIDIA HGXPCIeNVLinkNVSwitchThermal ManagementPower ManagementHigh-Performance AI Fabrics

Industry Keywords

GPU ClustersDistributed TrainingIntra-Node CommunicationValidation StrategiesDeployment Environments

Tech Stack

Tools & technologies

LinuxNode.js

About the role

Key responsibilities & impact

Define lifecycle standards for NVIDIA HGX platforms and validated baselines for firmware, BIOS, BMC, OS, drivers, CUDA, and NCCL
Own compatibility requirements and model GPU/CPU/memory/PCIe/NVLink/NVSwitch/NIC/storage relationships
Decide testing required for platform changes before production
Design qualification and burn-in strategies for new clusters, GPU platforms, repaired nodes, and major changes
Analyze fleet-wide distributions and outliers to identify isolated versus systemic hardware, firmware, software, topology, power, thermal, or test-configuration issues
Interpret NVIDIA diagnostics and telemetry across GPU, NVLink, NVSwitch, PCIe, memory, network, thermal, and power signals
Lead root-cause analysis for intermittent, multi-node, and fleet-wide issues
Define representative training and inference workloads and reproducible baselines for throughput, scaling, latency, and numerical behavior
Determine whether performance degradation originates in compute, communication, storage, scheduling, or configuration
Integrate work across Network, Storage, Platform/DevOps, and Facilities domains
Route issues to appropriate specialists with supporting evidence
Define health, readiness, quarantine, and return-to-service gates
Guide automation teams in building reproducible workflows and interpret validation results
Translate customer requirements into validation strategies and classify platforms as ready, degraded, conditional, or unsuitable
Define remediation and re-test scope and communicate risks to engineering, vendors, customers, and leadership

Requirements

What you’ll need

Strong hands-on experience with NVIDIA GPU infrastructure, including HGX or comparable large GPU server platforms
Demonstrated experience designing, executing, or leading burn-in and qualification across multi-node GPU clusters on H100, H200, B200, B300, or a comparable platform
Experience analyzing fleet-level qualification results, performance distributions, and hardware/software failures
Strong Linux systems administration and troubleshooting
Experience with NVIDIA drivers, CUDA, NCCL, DCGM, and GPU platform tooling
Experience diagnosing GPU, NVLink, NVSwitch, PCIe, thermal, power, or firmware problems
Experience validating intra-node and multi-node GPU communication, with a working understanding of RDMA, GPUDirect RDMA, and high-performance AI fabrics
Experience deploying, provisioning, or operationalizing GPU clusters
Experience defining firmware, BIOS, OS, and software baselines, as well as health, quarantine, readiness, and return-to-service criteria
Experience interpreting distributed training or inference workload behavior
Ability to troubleshoot performance across GPU, host, network, storage, and software boundaries
Ability to define requirements for automation teams and interpret automated validation outputs
Strong root-cause analysis and problem-solving skills
Clear communication of technical findings, risk, and remediation requirements
Comfortable in fast-moving deployment, commissioning, and production environments
English and Spanish proficiency levels are requested in the application form
Must currently live in Medellín or be willing to relocate there