Remote AI Infrastructure Engineer - GPU & Platform Scaling

Bright Vision Technologies; Oregon; Híbrido; USD 100.000 - 160.000; Jornada completa

Descripción de la vacante

Bright Vision Technologies is seeking an AI Infrastructure Engineer to design, build, and operate the platform layer powering large-scale AI workloads. The role centers on GPU clusters, distributed training, scheduling, storage performance, and developer experience for ML engineers and researchers, with emphasis on reliability, efficiency, and cost control.

The ideal candidate has built or operated production AI infrastructure at scale and understands hardware–kernel–ML framework interaction,

Formación

• Bachelor's or Master's degree in Computer Science or related field.
• Ten+ 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 (Go or C++).
• Deep understanding of distributed training, accelerator architectures, and collective communication.
• Experience with Kubernetes, Slurm, Ray, or similar ML scheduling systems.
• 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.

Responsabilidades

• 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.

Conocimientos

Python

Go

C++

Distributed training

Kubernetes

Slurm

Ray

Linux

CI/CD

Cloud ML

Educación

Bachelor's or Master's in Computer Science

Herramientas

Slurm

Ray

NVIDIA CUDA

NCCL

Descripción del empleo

Bright Vision Technologies is seeking an AI Infrastructure Engineer to design, build, and operate the platform layer powering large-scale AI workloads. The role centers on GPU clusters, distributed training, scheduling, storage performance, and developer experience for ML engineers and researchers, with emphasis on reliability, efficiency, and cost control.

The ideal candidate has built or operated production AI infrastructure at scale and understands hardware–kernel–ML framework interaction,