Role OverviewWe are seeking an AI Infrastructure Engineer to design, build, and operate the platform layer that powers large-scale AI training and inference workloads. The role focuses on GPU clusters, distributed training frameworks, scheduling, storage performance, and developer experience for ML engineers and researchers, with strong emphasis on reliability, efficiency, and cost control.
What You Will Do
Design and operate GPU and accelerator infrastructure for training and inference, build scheduling, queueing, and resource-sharing systems, integrate frameworks, operate high-performance storage systems, design networking architectures, build observability, implement checkpointing, restart, and fault-tolerance patterns, drive cost optimization, develop developer tooling, and partner with research and applied ML teams.
Why It Might Be a Fit
The ideal candidate has built or operated production AI infrastructure at scale, understands the interaction between hardware, kernel, scheduler, and ML framework, and brings strong software engineering discipline to platform work.
Requirements
- Bachelor’s or Master’s degree in Computer Science or a related field
- Six or more years of experience in infrastructure, platform, or HPC engineering
- Hands-on experience operating GPU clusters or large-scale ML training infrastructure
- Strong proficiency in Python and at least one systems language such as Go or C++
- Deep understanding of distributed training, accelerator architectures, and collective communication
- Experience with Kubernetes, Slurm, Ray, or similar scheduling systems for ML workloads
- Strong understanding of Linux internals, networking, and high-performance storage
- Experience with at least one major cloud provider’s ML infrastructure offerings
- Strong software engineering practices including testing, CI/CD, and code review
- Excellent communication and cross-functional collaboration skills
Benefits
- Competitive base salary commensurate with experience, plus benefits
- 100% remote work
- Long-term, multi-year employment
- Direct W2 employment with Bright Vision Technologies
- No C2C, 1099, or third-party arrangements
- H1B transfers welcomed for qualified candidates
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