AI Infrastructure Engineer

• Design, deploy, and operate GPU and accelerator infrastructure supporting large-scale AI training and inference workloads across cloud, on-premise, and hybrid environments.
• Build and optimize scheduling, queueing, and resource-sharing systems to maximize accelerator utilization across multiple teams and workloads.
• Integrate distributed training frameworks such as PyTorch, JAX, DeepSpeed, FSDP, Megatron-LM, and Ray Train into scalable platform environments.
• Manage high-performance storage systems and data pipelines to ensure efficient delivery of training data to compute infrastructure.
• Design and maintain networking architectures supporting RDMA, InfiniBand, NCCL, and high-bandwidth distributed communication protocols.
• Develop observability, monitoring, and analytics solutions for AI workloads, including utilization metrics, throughput analysis, and training stability monitoring.
• Implement checkpointing, fault tolerance, restart mechanisms, and resiliency strategies for long-running distributed training jobs.
• Drive infrastructure cost optimization initiatives across compute, storage, networking, and cloud resource utilization.
• Create developer tooling, automation workflows, and self-service platform capabilities to improve productivity for ML engineers and researchers.
• Partner with AI research and applied ML teams to support capacity planning, infrastructure scaling, and workload forecasting.
• Implement security controls, multi-tenant isolation strategies, and access management policies for enterprise AI infrastructure environments.
• Automate provisioning, lifecycle management, configuration enforcement, and operational workflows across AI infrastructure platforms.
• Maintain operational documentation, runbooks, dashboards, and platform standards to support scalability and long-term maintainability.
• Stay current with emerging AI infrastructure technologies, accelerator hardware advancements, and open-source ecosystem developments.

Requirements:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
• Minimum of 6 years of experience in infrastructure engineering, platform engineering, high-performance computing, or related domains.
• Hands-on experience operating GPU clusters and large-scale machine learning infrastructure in production environments.
• Strong programming skills in Python and at least one systems programming language such as Go or C++.
• Deep understanding of distributed training architectures, accelerator technologies, and collective communication frameworks.
• Experience with Kubernetes, Slurm, Ray, or comparable orchestration and scheduling systems for ML workloads.
• Strong expertise in Linux internals, networking, storage systems, and distributed systems operations.
• Experience working with major cloud providers and cloud-native AI infrastructure services.
• Solid understanding of software engineering best practices including CI/CD, testing, automation, and code review processes.
• Excellent troubleshooting, analytical, documentation, and cross-functional collaboration skills.
• Experience operating RDMA or InfiniBand networking environments is preferred.
• Contributions to open-source ML infrastructure projects or experience with research-scale AI platforms are considered a plus.
• Familiarity with AI infrastructure cost optimization and FinOps practices is advantageous.
• Candidates must be authorized to work in the United States. No new H1B sponsorship is available, though H1B transfers may be supported for qualified candidates.
• Ability to successfully complete technical coding assessments related to infrastructure engineering and distributed systems.

Benefits:

• 100% remote work opportunity within the Continental United States.
• Competitive compensation package aligned with experience and technical expertise.
• Full-time W2 employment with long-term project stability.
• Opportunity to work on advanced AI infrastructure and cutting-edge machine learning platforms.
• Exposure to large-scale distributed systems, GPU technologies, and enterprise AI operations.
• Strong career growth and professional development opportunities.
• Collaborative and innovation-focused engineering environment.
• Equal opportunity workplace committed to diversity, inclusion, and accessibility.
• Comprehensive employee benefits and support programs.