Set technical direction for and lead the design, build, and operation of large-scale, multi-tenant GPU/ML training, inference, rendering, and general compute infrastructure on Kubernetes Drive cross-team and org-level initiatives that improve cluster reliability, scheduling efficiency, utilization, and scale, including datacenter and hardware migrations and expansions Our reliability for the cluster end to end: participate in and improve oncall and incident response, and build the automation and observability that reduces operational costs Define and drive best practices in system design, testing, code health, and operational excellence across the team Partner with research and infrastructure teams to translate ML workload requirements into platform capabilities, and influence architecture and technical decisions beyond your immediate team Mentor engineers, review designs, and improve the overall technical quality of the team's work through code reviews, design guidance, and knowledge sharingBachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 8+ years of professional software engineering experience, including technical leadership of complex, cross-team infrastructure efforts Experience building and maintaining production systems in Python (and/or Go, C++, or a similar language) Proven experience designing, operating, and scaling large distributed systems in production, including production reliability / oncall ownership Hands-on experience running container orchestration (Kubernetes or comparable) at scale Experience with one or more of: GPU/ML training infrastructure, cluster/batch scheduling, distributed storage, or high-performance networking Demonstrated ability to drive ambiguous, organization-level technical problems to resolution and to set multi-quarter technical direction Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Large-scale distributed or parallel storage systems (e.g., HDFS, Isilon, Lustre) Deep Linux systems experience, performance tuning, and observability (metrics, tracing, alerting) GPU cluster operations (NVIDIA DGX, H100/A100), NCCL, and high-performance networking (InfiniBand / RoCE) Distributed ML training frameworks (e.g., PyTorch) and the surrounding training/inference stack Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Kubernetes operators/CRDs and batch schedulers (e.g., Volcano, Slurm, Kueue) Experience with Hack/PHP Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)Meta builds technologies that help people connect, find communities, and grow businesses. Our research team has built and operates a shared ML compute platform: a large-scale, Kubernetes-based multi-use cluster that powers model training, inference, rendering, and unique research workloads with interactive development for research and engineering teams across RL-R.