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# Software Engineer, ML Infrastructure, Reality Labs Research

- **Company:** The Meta Game, Inc.
- **Location:** Seattle, WA, United States
- **Experience:** Expert
- **Salary:** $183,997.0 - $257,000.0
- **Contract:** Permanent contract
- **Skills:** PHP (Programming Language), Artificial Intelligence, C++ (Programming Language), Code Review, Computer Engineering, Linux, Distributed Data Store, Distributed Systems, Hadoop Distributed File System, InfiniBand, Python (Programming Language), Machine Learning, Performance Tuning, Software Engineering, Pytorch, Prompt Engineering, Kubernetes, Information Technology, Slurm, Machine Learning Operations, Isilon
- **Published:** October 15, 2026
- **Apply:** https://dejobs.org/x/x/01856FDD4CFE454BA8DA8E834EC65218/job/

## About the Role

7. Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
 8. 8+ years of professional software engineering experience, including technical leadership of complex, cross-team infrastructure efforts
 9. Experience building and maintaining production systems in Python (and/or Go, C++, or a similar language)
 10. Proven experience designing, operating, and scaling large distributed systems in production, including production reliability / oncall ownership
 11. Hands-on experience running container orchestration (Kubernetes or comparable) at scale
 12. Experience with one or more of: GPU/ML training infrastructure, cluster/batch scheduling, distributed storage, or high-performance networking
 13. Demonstrated ability to drive ambiguous, organization-level technical problems to resolution and to set multi-quarter technical direction, 14. Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
 15. Large-scale distributed or parallel storage systems (e.g., HDFS, Isilon, Lustre)
 16. Deep Linux systems experience, performance tuning, and observability (metrics, tracing, alerting)
 17. GPU cluster operations (NVIDIA DGX, H100/A100), NCCL, and high-performance networking (InfiniBand / RoCE)
 18. Distributed ML training frameworks (e.g., PyTorch) and the surrounding training/inference stack
 19. Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
 20. Kubernetes operators/CRDs and batch schedulers (e.g., Volcano, Slurm, Kueue)
 21. Experience with Hack/PHP
 22. Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

## Description

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.Meta is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, you may contact us at accommodations-ext@fb.com, 1. 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

 2. Drive cross-team and org-level initiatives that improve cluster reliability, scheduling efficiency, utilization, and scale, including datacenter and hardware migrations and expansions
 3. 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
 4. Define and drive best practices in system design, testing, code health, and operational excellence across the team
 5. Partner with research and infrastructure teams to translate ML workload requirements into platform capabilities, and influence architecture and technical decisions beyond your immediate team
 6. Mentor engineers, review designs, and improve the overall technical quality of the team's work through code reviews, design guidance, and knowledge sharing