Senior AI Infrastructure Storage Engineer
Active AI foundation-model company offering multimodal models, consumer AI products, and an enterprise/developer API platform.
Funding history
About MiniMax Group Inc.
MiniMax develops proprietary multimodal foundation models spanning language, video, speech, and music, and operates AI-native products including MiniMax Agent, MiniMax Design, MiniMax Audio, Talkie, Hailuo AI, and an Open Platform.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will design, build, and evolve high-performance, scalable, reliable distributed storage systems for AI training and inference. You will optimize storage throughput, latency, concurrency, reliability, and cost; build efficient data paths across GPU, CPU, networks, and storage; and implement storage solutions for checkpoints, datasets, features, and inference data. You will also support capacity planning, stability, performance diagnosis, fault resolution, and cross-functional interface and tooling standards.
Requirements
- 5+ years of storage, distributed systems, or cloud infrastructure engineering experience
- Knowledge of operating systems, computer networking, concurrent programming, data structures, and algorithms
- Experience designing and developing file systems, object storage, block storage, or caching systems
- Proficiency in at least one of C, C++, Rust, or Go
- Knowledge of the Linux I/O stack, network protocols, and performance-analysis tools
- Ability to independently troubleshoot complex performance and stability issues
Responsibilities
- Design and develop AI infrastructure storage-system architecture and core modules
- Build distributed file systems, object storage, caching, and data-acceleration capabilities
- Optimize storage throughput, latency, concurrency, reliability, and cost efficiency
- Build efficient data read/write paths across GPUs, CPUs, networks, and storage
- Design and implement storage solutions for checkpoints, training datasets, feature data, and inference data
- Perform capacity planning, stability engineering, performance diagnosis, and fault resolution
- Collaborate on standardized interfaces, toolchains, and best practices