MLCommons
MLPerf Software Engineer
Design, build, and operate the systems, infrastructure, and automation behind MLCommons' MLPerf AI benchmarks — owning architecture through implementation of pipelines for benchmark submission, validation, review, and reproducibility. Core stack: Python, Bash, GitHub Actions CI/CD, and Docker, with ML benchmarking knowledge.
- Compensation
- Not disclosed
- Location
- Remote (North American time zone preferred)
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 4+ years
- Category
- Software engineering
- Posted
Skills
Must have
- Experience designing systems and infrastructure (architecture decisions, trade-off analysis, scalability, reliability)
- 4+ years of post-academic software engineering experience or equivalent open-source/industry impact
- Python
- Bash
- GitHub Actions or equivalent CI/CD
- Artifact and version management
- Docker containers
- ML concepts, including inference and training basics
- Performance benchmarking or optimization
Full description
You will design and build the systems, infrastructure, and automation that power MLPerf benchmarks. You will own technical decisions from architecture through implementation, improve existing pipelines, resolve complex issues, and document and present technical designs and outcomes.
Responsibilities
- Architect and design benchmarking systems, infrastructure, and tooling.
- Design scalable automation for benchmark submission, validation, review, publication, and reproducibility.
- Operate and improve existing MLPerf pipelines.
- Translate roadmap goals into technical designs and deliver them.
- Collaborate on end-to-end benchmarking pipelines.
- Debug and resolve issues across automation, submissions, and benchmark harnesses.
- Produce design documentation.
- Present technical designs, progress, and outcomes to working groups.
Requirements
- Experience designing systems and infrastructure, including architecture decisions, trade-off analysis, scalability, and reliability.
- 4+ years of post-academic software engineering experience or equivalent open-source or industry impact.
- Python.
- Bash.
- GitHub Actions or equivalent CI/CD.
- Artifact and version management.
- Docker containers.
- ML concepts, including inference and training basics.
- Performance benchmarking or optimization.
Benefits
- Fully remote work.
- Flexible working environment.