Member of Technical Staff - Training Cluster Engineer

Stellenbeschreibung:

Overview

Join to apply for the Member of Technical Staff - Training Cluster Engineer role at Black Forest Labs .

At Black Forest Labs, we're on a mission to advance the state of the art in generative deep learning for media, building powerful, creative, and open models that push what's possible. Born from foundational research, we continuously create advanced infrastructure to transform ideas into images and videos. Our team pioneered Latent Diffusion, Stable Diffusion, and FLUX.1 – milestones in the evolution of generative AI. Today, these foundations power millions of creations worldwide, from individual artists to enterprise applications. We are looking for a Training Cluster Engineer to join our fast growing team.

Responsibilities

Design, deploy, and maintain large-scale ML training clusters running SLURM for distributed workload orchestration
Implement comprehensive node health monitoring systems with automated failure detection and recovery workflows
Partner with cloud and colocation providers to ensure cluster availability and performance
Establish and enforce security best practices across the ML infrastructure stack (network, storage, compute)
Build and maintain developer-facing tools and APIs that streamline ML workflows and improve researcher productivity
Collaborate directly with ML research teams to translate computational requirements into infrastructure capabilities and capacity planning

What we look for

Production experience managing SLURM clusters at scale, including job scheduling policies, resource allocation, and federation
Hands-on experience with Docker, Enroot/Pyxis, or similar container runtimes in HPC environments
Proven track record managing GPU clusters, including driver management and DCGM monitoring

Preferred Qualifications

Understanding of distributed training patterns, checkpointing strategies, and data pipeline optimization
Experience with Kubernetes for containerized workloads, particularly for inference or mixed compute environments
Experience with high-performance interconnects (InfiniBand, RoCE) and NCCL optimization for multi-node training
Track record of managing 1000+ GPU training runs, with deep understanding of failure modes and recovery patterns
Familiarity with high-performance storage solutions (VAST, blob storage) and their performance characteristics for ML workloads
Experience running hybrid training/inference infrastructure with appropriate resource isolation
Strong scripting skills (Python, Bash) and infrastructure-as-code experience