AI Infrastructure Engineer

Job summary

Bri*** 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.

Benefits

Qualifications

• 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
• Experience operating InfiniBand or RDMA networking at scale
• Contributions to open-source ML infrastructure projects

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

Skills

Degrees

Industry

Company size