Software Engineer, ML Infrastructure, Reality Labs Research

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At Meta Reality Labs Research (RL-R), our goal is to explore, innovate, and design novel interfaces and hardware subsystems for the next generation of virtual, augmented, and mixed reality experiences. 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.

We are looking for a senior software engineer to set technical direction for this platform, driving its reliability, scale, and efficiency as demand grows, and partnering across teams to turn evolving ML workload needs into platform capabilities. In this role, you will be a hands-on technical leader who has experience navigating ambiguous problem spaces and driving them to concrete solutions, operates complex distributed systems in production, and elevates team practices through code reviews, design feedback, and knowledge sharing.

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• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience8+ 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 (Infini Band / 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)