GPU & ML Infrastructure Engineer

Svitla Systems, Inc.

Що робити

Port test procedure to new hardware. Datacenter GPUs and edge devices differ in their driver stacks, power, thermal envelopes, and available sensors. Adapt the procedure, document what changed and why.
Build and maintain the benchmark workload suite: synthetic kernels (GEMM, convolution), LLM inference and training, and new classes as target classes expand, including vision models for the edge. These are controlled load generators for hardware characterization.
Own logger correctness and consistency. Make collection predictable across platforms and sources: maintain intended sampling rates, ensure trustworthy timestamps, use consistent field names, and align clocks between in-band and out-of-band sources. Diagnose sampling anomalies to root cause.
Automate deployment. Repeatable, unattended process: stand up workloads and loggers on a new node, execute the run schedule, collect, and tear down cleanly. Runs are reproducible from configuration.
Build automated data quality checks. Catch missing or irregular samples, clock mismatches between loggers, telemetry that doesn’t align with the workload, and sensors silently returning no data on new hardware.

Experience deploying LLM inference and training workloads on GPUs, including quantized models.
Demonstrated ability to diagnose sensor and sampling problems in time-series hardware data independently.
Understanding of how to make a GPU workload reproducible run-to-run: controlling sources of nondeterminism.
Strong understanding of Linux systems, GPU driver stacks, process orchestration, scheduling, and timing behavior.
Experience collecting hardware telemetry programmatically, in-band (NVML, DCGM) and out-of-band (BMC /IPMI / Redfish).