General Manager MLPerf
MLCommons is a global non-profit AI engineering consortium that develops open benchmarks, datasets, data standards, and related tooling for measuring AI performance, quality, safety, and risk.
Funding history
Investors
About MLCommons
MLCommons collaborates with industry, academia, non-profits, and other members to improve AI systems through reproducible benchmark suites, safety evaluation, open datasets, and data standards. Its work includes MLPerf benchmarks, AILuminate AI safety benchmarks, Croissant dataset metadata, and MLCube portability conventions.
Skills
About the Role
You will own the MLPerf strategy, financial outcomes, roadmap, and benchmark lifecycle. You will lead staff, build consensus among stakeholders, contribute to benchmark design and results analysis, cultivate board and member relationships, and align technical priorities with business value.
Requirements
- Product management experience, ideally in technical standards, evaluation platforms, or AI developer tools.
- Deep understanding of AI and machine-learning systems, benchmarking, and performance engineering.
- Strategic understanding of the AI ecosystem and performance metrics.
- Analytical skills and judgment for trade-offs in a multi-stakeholder environment.
- Communication skills bridging engineering and non-technical stakeholders.
- Experience with enterprise procurement or translating technical product data into business or economic value.
- Familiarity with AI hardware and inference and training software stacks.
- Experience with a consortium, standards body, open-source community, or other multi-stakeholder environment.
Responsibilities
- Own the MLPerf P&L and overall strategy.
- Map the strategic roadmap to financial outcomes.
- Drive revenue generation through community cultivation.
- Set investment priorities and align technical roadmap and product development with financial sustainability.
- Partner with working groups to define, prioritize, and execute the benchmark roadmap.
- Cultivate member and board relationships.
- Improve the benchmark lifecycle while maintaining reproducibility and transparent governance.
- Build consensus among technical working groups and stakeholders.
- Contribute to benchmark design, rules definition, and results analysis.
- Build and lead a product-focused team.
- Partner with engineering to ensure high-quality delivery.
Benefits
- Remote-first work.
- Flexible working environment.
Hiring Process
Apply by emailing jobs@mlcommons.org with your resume and relationships for three professional references; include the job title in the subject line. References will not be contacted without permission.