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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Systems Engineer - **Company:** Bright Vision Technologies - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $100,000.0 - $150,000.0 - **Contract:** Permanent contract - **Skills:** Training Data, Artificial Intelligence, C++ (Programming Language), Computer Clusters, Code Review, Continuous Integration, Distributed Computing Environment, Fault Tolerance, Identity and Access Management, InfiniBand, Python (Programming Language), Linux Kernel, Machine Learning, Network Architecture, Open Source Technology, Remote Direct Memory Access, Runbook, Software Engineering, AI Infrastructure, Pytorch, AI Platforms, Kubernetes, Information Technology, Slurm, Machine Learning Operations, Data Pipelines - **Published:** July 19, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=8d2dcd6e1fd69b1e ## About the Role Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position., * 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., * 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. ## Description We are seeking an AI Systems 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. 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., * 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. ## Related Videos - [Running Secure Life Science Research at Scale using Hybrid GPU HPC and Kubernetes 🧬](https://www.wearedevelopers.com/videos/100355-running-secure-life-science-research-at-scale-using-hybrid-gpu-hpc-and-kubernetes) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Technical Documentation - How Can I Write Them Better and Why Should I Care?](https://www.wearedevelopers.com/videos/681-technical-documentation-how-can-i-write-them-better-and-why-should-i-care) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models) - [AI Factories at Scale](https://www.wearedevelopers.com/videos/1139-ai-factories-at-scale) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [Got AI ideas but no money? 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