Remote ML Infra Engineer - Scale AI Training & GPU Systems

Bright Vision Technologies; Sugar Land (TX); A distancia; USD 100.000 - 150.000; Jornada completa

Descripción de la vacante

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

The ideal candidate has built or operated production AI infra at scale and understands hardware, kernel, scheduler, and ML framework interactions,

Formación

• Bachelor’s or Master’s degree in CS or related field (required).
• 6+ years of infrastructure, platform, or HPC engineering experience.
• Hands-on experience operating GPU clusters or large-scale ML training infra.
• Strong Python and at least one systems language (Go or C++).
• Deep understanding of distributed training, accelerators, and collective communication.
• Experience with Kubernetes, Slurm, Ray, or similar ML scheduling systems.
• Strong Linux, networking, and HPC storage knowledge.
• Experience with at least one major cloud provider’s ML infra offerings.
• Strong software engineering practices including testing, CI/CD, and code review.
• Excellent communication and cross-functional collaboration.

Responsabilidades

• Design and operate GPU and accelerator infrastructure for training and inference across on‑prem, cloud, and hybrid setups.
• Build scheduling, queueing, and resource-sharing systems to maximize accelerator utilization.
• Integrate PyTorch, JAX, DeepSpeed, FSDP, Megatron-LM, and Ray Train into a unified platform.
• Operate high-performance storage systems and data pipelines for near-line-rate data feeds.
• Design networking architectures supporting RDMA, InfiniBand, NCCL, and high-bandwidth communication.
• Build observability for AI workloads including utilization, throughput, training stability, and failure analytics.
• Implement checkpointing, restart, and fault-tolerance patterns for long-running training jobs.
• Drive cost optimization across compute, storage, and networking via scheduling and spot capacity.
• Develop tooling and paved-road workflows for researchers to launch experiments safely.
• Collaborate with research teams to plan capacity for upcoming training runs.
• Implement security controls, isolation, and access management for multi-tenant infra.
• Automate cluster provisioning, lifecycle management, and configuration enforcement.
• Maintain runbooks, capacity dashboards, and operational docs for the AI platform.
• Stay current with AI infra research, accelerators, and open-source tooling.

Educación

Bachelor’s or Master’s in Computer Science or related field

Herramientas

PyTorch

JAX

DeepSpeed

FSDP

Megatron-LM

Ray Train

Kubernetes

Slurm

Ray

Descripción del empleo

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

The ideal candidate has built or operated production AI infra at scale and understands hardware, kernel, scheduler, and ML framework interactions,