AI Infrastructure Engineer
Bright Vision Technologies•Fremont, CA
•Remote
About The Position
Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge technologies to create scalable, secure, and user-friendly applications. As we continue to grow, we’re looking for a skilled AI Infrastructure Engineer to join our dynamic team and contribute to our mission of transforming business processes through technology. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.
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.
Nice To Haves
- Experience operating InfiniBand or RDMA networking at scale.
- Contributions to open-source ML infrastructure projects.
- Familiarity with custom orchestrators or research-grade training stacks.
- Exposure to frontier model training operations.
- Experience with FinOps for AI workloads.
Responsibilities
- Design and operate GPU and accelerator infrastructure for training and inference, spanning on-prem clusters, cloud-managed services, and hybrid configurations.
- Build scheduling, queueing, and resource-sharing systems that maximize accelerator utilization across many teams.
- Integrate frameworks such as PyTorch, JAX, DeepSpeed, FSDP, Megatron-LM, and Ray Train into a unified platform offering.
- Operate high-performance storage systems and data pipelines that keep accelerators fed with training data at near-line-rate.
- Design networking architectures supporting RDMA, InfiniBand, NCCL, and high-bandwidth collective communication.
- Build observability for AI workloads including utilization, throughput, training stability, and failure-mode analytics.
- Implement checkpointing, restart, and fault-tolerance patterns for long-running training jobs at scale.
- Drive cost optimization across compute, storage, and networking through scheduling, spot capacity, and right-sizing.
- Develop developer tooling and paved-road workflows that let researchers launch experiments safely and efficiently.
- Partner with research and applied ML teams to plan capacity for upcoming training runs.
- Implement security controls, isolation, and access management for multi-tenant AI infrastructure.
- Drive automation across cluster provisioning, lifecycle management, and configuration enforcement.
- Maintain runbooks, capacity dashboards, and operational documentation for the AI platform.
- Stay current with AI infrastructure research, accelerator hardware, and emerging open-source AI tooling.
Benefits
- Competitive base salary commensurate with experience, plus benefits.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
