A managed benchmarking and regression triage service for teams validating AI silicon, firmware, and model performance before release.
Added Jul 15, 2026
Medium opportunity (59%)
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Hardware and AI infrastructure teams need to understand how real workloads behave across new silicon, firmware, and model stacks. The job signals repeatedly mention power analysis, stress workloads, performance bottlenecks, numerical correctness, live regression dashboards, and automated hardware or firmware triage. These are specialized workflows that many chip, device, and AI systems teams struggle to staff and operationalize consistently.
Start as a managed characterization service that ports customer workloads, runs repeatable power and performance test suites, and delivers regression reports with threshold alerts and suspected root causes. The first product can be a standardized lab workflow: ingest workloads, define benchmarks, collect telemetry, compare runs, and produce engineering-ready triage packets. Over time, the repeatable pieces can become a hybrid toolchain plus services business for silicon validation teams.
AI accelerators, edge AI chips, and custom silicon programs are growing, while workload behavior increasingly spans models, firmware, drivers, networking, and power envelopes. Teams are hiring for this capability because generic observability tools do not solve silicon-level workload characterization.
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Showing 1-20 of 39 signals
You’ll join the AI Performance Tooling team within Wayve’s AI Performance organization, which makes model training and inference faster, more efficient, and more predictable across cloud and embedded hardware. Our mission is to enable data-driven AI performance decisions across priority workloads and hardware targets: Measure performance teams can trust; Monitor trends and catch regressions; Predict the cost of changes before we run them; Advise on bottlenecks and prioritized opportunities. You’
Identify and help resolve ASIC, board, and firmware issues; provide diagnostics support to internal engineering teams and external customers Develop and deploy AI-based tools and workflows to accelerate test development, failure triage, and debug
Develop tools, software, and automation frameworks to improve AI workload observability, performance analysis, scalability testing, and benchmark execution. Collaborate with customers, partners, and internal engineering organizations to characterize, troubleshoot, and optimize AI solutions deployed on HPE infrastructure.
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