Machine Learning Infrastructure Engineer
About the role We’re looking for seasoned ML Infrastructure engineers with experience designing, building and maintaining training and serving infrastructure for ML research. Responsibilities : Provide infrastructure support to our ML research and product Build tooling to diagnose cluster issues and hardware failures M
What this role actually needs.
About the role We’re looking for seasoned ML Infrastructure engineers with experience designing, building and maintaining training and serving infrastructure for ML research. Responsibilities : Provide infrastructure support to our ML research and product Build tooling to diagnose cluster issues and hardware failures M Responsibilities: - Provide infrastructure support to our ML research and product - Build tooling to diagnose cluster issues and hardware failures - Monitor deployments, manage experiments, and generally support our research - Maximize GPU allocation and utilization for both serving and training - 4+ years of experience supporting the infrastructure within an ML environment - Experience in developing tools used to diagnose ML infrastructure problems and failures Requirements: - 4+ years of experience supporting the infrastructure within an ML environment - Experience in developing tools used to diagnose ML infrastructure problems and failures - Experience with cloud platforms (e.g., Compute Engine, Kubernetes, Cloud Storage) - Experience working with GPUs - Experience with large GPU clusters and high-performance computing/networking - Experience with supporting large language model training Company context: Character.AI builds personalized conversational AI agents and the infrastructure that powers them at scale.
Day-to-day expectations
Character.AI lists these responsibilities for the Machine Learning Infrastructure Engineer role.
- Provide infrastructure support to our ML research and product
- Build tooling to diagnose cluster issues and hardware failures
- Monitor deployments, manage experiments, and generally support our research
- Maximize GPU allocation and utilization for both serving and training
- 4+ years of experience supporting the infrastructure within an ML environment
- Experience in developing tools used to diagnose ML infrastructure problems and failures
What a strong candidate brings
These requirements are extracted from the source listing and normalized for UpJobz readers.
- 4+ years of experience supporting the infrastructure within an ML environment
- Experience in developing tools used to diagnose ML infrastructure problems and failures
- Experience with cloud platforms (e.g., Compute Engine, Kubernetes, Cloud Storage)
- Experience working with GPUs
- Experience with large GPU clusters and high-performance computing/networking
- Experience with supporting large language model training
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Hybrid roles in Redwood City should be compared against commute, local salary bands, and nearby employer demand.
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- Use open to tn, h-1b, and opt candidates already in the united states as part of your positioning so the recruiter does not have to infer it.
- Show concrete examples of succeeding in hybrid environments.
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Source: jobs.ashbyhq.com · Source ID: 13006848-6a69-4467-9274-5d54e80b965d · Confidence: 92/100 · Last checked: May 7, 2026
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