Senior ML Infrastructure Engineer (Kubernetes & Scale)

Meta; Redmond, Seattle (WA, WA); On-site; USD 170,000 - 250,000; Full time

Job summary

Meta Reality Labs Research (RL-R) is seeking a senior software engineer to set the technical direction for our large-scale Kubernetes-based ML compute platform. You will drive reliability, scale, and efficiency, partnering across RL-R and engineering teams to turn evolving ML workload needs into concrete platform capabilities.

The role demands hands-on leadership, experience operating complex distributed systems in production, and collaboration to improve tooling, observability, and performance

Qualifications

• 8+ years of professional software engineering experience, including technical leadership of cross-team infrastructure.
• Experience building and maintaining production systems in Python, Go, C++, or similar languages.
• Proven ability to design, operate, and scale large distributed systems in production.
• Hands-on experience running Kubernetes at scale and using GPU/ML training infrastructure.
• Familiarity with HPC networking, storage, and high-performance networking concepts.

Responsibilities

• Set technical direction for the ML compute platform powering model training, inference, and rendering.
• Drive reliability, scale, and efficiency as demand grows; partner across teams to turn ML workload needs into platform capabilities.
• Elevate team practices through code reviews, design feedback, and knowledge sharing.

Skills

Software engineering

Technical leadership

Distributed systems

Kubernetes

Education

Bachelor's degree in Computer Science, Computer Engineering, relevant field

Tools

Kubernetes

PyTorch

NVIDIA DGX

Job description

Meta Reality Labs Research (RL-R) is seeking a senior software engineer to set the technical direction for our large-scale Kubernetes-based ML compute platform. You will drive reliability, scale, and efficiency, partnering across RL-R and engineering teams to turn evolving ML workload needs into concrete platform capabilities.

The role demands hands-on leadership, experience operating complex distributed systems in production, and collaboration to improve tooling, observability, and performance