What youll be doing:
Develop and run software tools, automation, and workloads to characterize power, performance, and drive behavior across our rack-scale systems.
Execute AI and system-level workloads to stress and evaluate behavior across the stack, including GPUs, CPUs, networking, storage, firmware, drivers, and system software.
Build automated frameworks for data collection, telemetry, validation, correlation, and analysis of characterization results.
Investigate system behavior under different workloads and operating conditions to identify bottlenecks, anomalies, and optimization opportunities.
Work closely with hardware, firmware, driver, system software, performance, and validation teams to define characterization methodologies and debug cross-stack issues.
Support bring-up, validation, and readiness activities for new rack-scale platforms and AI infrastructure.
Create clear documentation, test flows, and repeatable processes to improve coverage, efficiency, and reproducibility.
What we need to see:
B.Sc. or M.Sc. in Computer Science, Electrical Engineering, or a related field.
5+ years of software engineering experience, preferably in system software, infrastructure, validation, or performance-focused environments.
Strong programming skills in Python and at least one system-level language such as C/C++.
Experience developing automation and test infrastructure for complex hardware/software systems.
Hands-on experience running, debugging, or optimizing AI, HPC, or large-scale system workloads.
Good understanding of system-level architecture, including interactions across hardware, firmware, drivers, operating systems, and application layers
Experience working in Linux environments and with scripting, telemetry, logging, and data analysis tools.
Strong debugging and problem-solving skills, with the ability to work across multiple engineering disciplines.
Good communication skills and the ability to drive technical work in a fast-paced, cross-functional environment.
Ways to stand out from the crowd:
Experience with our platforms, GPU systems, or rack-scale AI infrastructure.
Background in power, thermal, performance, or storage/drive characterization.
Experience with workload automation, cluster orchestration, or lab infrastructure.
Familiarity with AI benchmarks, training/inference workloads, and system stress methodologies.
Experience in post-silicon validation, production testing, or system bring-up.
