GPU & ML Infrastructure Engineer

Svitla Systems, Inc.; Argentina; Híbrido; ARS 182.877.000 - 274.315.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Remote or office workspace

Technical webinars and meetups

Bonuses for talks and activities

Regular online activities

Descripción de la vacante

Svitla Systems, Inc. is seeking a GPU & ML Infrastructure Engineer for a full-time role in the USA. You will own end-to-end data generation for GPU systems, including benchmarking workloads, automated deployment, hardware telemetry, data quality checks, and dataset delivery.

You will work with NVIDIA data-center GPUs across generations and with edge platforms. The ideal candidate has strong Linux expertise, experience with LLM workloads on GPUs, and a track record of building reproducible GPU

Formación

• Strong Linux and GPU driver stack knowledge.
• Experience deploying LLM workloads on GPUs (training and inference).
• Experience collecting hardware telemetry programmatically (NVML/DCGM/IPMI/Redfish).
• Ability to work independently and overlap with client team until 11:00 a.m. PST.

Responsabilidades

• Port test procedures to new data-center GPUs and edge devices.
• Build and maintain GPU benchmark workloads incl. synthetic kernels and LLM workloads.
• Ensure telemetry collection is accurate across platforms and data sources.
• Investigate sampling issues, timestamp inconsistencies, and missing sensor data.
• Automate workload deployment, execution, data collection, and environment cleanup.
• Create automated checks for missing samples and clock mismatches.
• Deliver datasets with complete run metadata and documentation of hardware targets.

Conocimientos

Linux systems

GPU driver stacks

Python automation

Telemetry collection

Herramientas

NVML

DCGM

IPMI

Redfish

Descripción del empleo

Svitla Systems Inc. is looking for a GPU & ML Infrastructure Engineer for a full-time position (40 hours per week) in the USA. Our client is a stealth startup. The successful candidate will own the end-to-end data generation process for GPU systems, including benchmark workloads, automated deployment, hardware telemetry collection, data quality validation, and dataset delivery. The role requires working with multiple NVIDIA data-center GPU generations and embedded or edge platforms.
Requirements

• Strong experience deploying LLM inference and training workloads on GPUs, including quantized models.
• Experience diagnosing sensor, logging, and sampling issues in time-series hardware data.
• Ability to build reproducible GPU workloads and control sources of run-to-run variation.
• Strong Linux systems knowledge, including GPU driver stacks, process orchestration, scheduling, and timing.
• Experience collecting hardware telemetry programmatically using NVML, DCGM, BMC, IPMI, or Redfish.
• Strong Python skills for automation, telemetry collection, and data processing.
• Experience automating workload deployment and data collection across different hardware platforms.
• Ability to work independently and take ownership of technical processes.
• Availability to overlap with the client’s team until 11:00 a.m. PST.

Nice to have

• Experience building data collection pipelines for hardware testing or systems research.
• Familiarity with GPU benchmarking, stress testing, and benchmark methodology.
• Knowledge of GPU power, thermal management, multi-GPU scaling, and NCCL.
• Experience building automated data quality checks for time-series or sensor data.

Responsibilities

• Port existing test procedures to new data-center GPUs and edge devices.
• Build and maintain GPU benchmark workloads, including synthetic kernels and LLM inference and training.
• Ensure telemetry collection is accurate and consistent across platforms and data sources.
• Investigate sampling issues, timestamp inconsistencies, missing sensor data, and logging anomalies.
• Automate workload deployment, execution, data collection, and environment cleanup.
• Build automated checks for missing samples, irregular intervals, clock mismatches, and invalid telemetry.
• Deliver datasets in a consistent and documented format with complete run metadata.
• Document hardware targets, configurations, procedures, driver versions, and firmware versions.

WE OFFER

• US and EU projects based on advanced technologies.
• Competitive compensation based on skills and experience.
• Flexibility in workspace, either remote or our welcoming office.
• Bonuses for article writing, public talks, and other activities.
• Free tech webinars and meetups organized by Svitla.
• Regular corporate online activities.
• Awesome team, friendly and supportive community!