MLCommons is a global non-profit AI engineering consortium that develops open benchmarks, datasets, and data tooling to measure and improve AI quality, performance, safety, speed, efficiency, and risk.
Daily open opportunities, newly observed postings, hiring breadth, and normalized monthly compensation for jobs in this pillar.
As of 2026-11-06
Distinct employers
+0 vs prior week
7 days vs previous 7 days
Open roles, employers, and newly listed jobs by day
Earlier open counts are estimated from observation dates. Offline postings are never inferred open for more than 90 days; new daily samples are exact snapshots.
MLCommons is a global non-profit AI engineering consortium that develops open benchmarks, datasets, and data tooling to measure and improve AI quality, performance, safety, speed, efficiency, and risk.
Funding history
Investors
MLCommons brings together industry, academia, non-profits, and other members through open collaborative engineering. Its work includes industry-standard MLPerf benchmarks, AI risk and reliability evaluation through AILuminate, and data standards and datasets such as Croissant and MLCube.
MLCommons brings together industry, academia, non-profits, and other members through open collaborative engineering. Its work includes industry-standard MLPerf benchmarks, AI risk and reliability evaluation through AILuminate, and data standards and datasets such as Croissant and MLCube.
MLCommons·1 month ago
MLCommons is a global non-profit AI engineering consortium that develops open benchmarks, datasets, and data tooling to measure and improve AI quality, performance, safety, speed, efficiency, and risk.
Investors
MLCommons·1 month ago
MLCommons is a global non-profit AI engineering consortium that develops open benchmarks, datasets, and data tooling to measure and improve AI quality, performance, safety, speed, efficiency, and risk.
Investors
MLCommons·1 month ago
MLCommons is a global non-profit AI engineering consortium that develops open benchmarks, datasets, and data tooling to measure and improve AI quality, performance, safety, speed, efficiency, and risk.
Investors
