Staff AI Runtime Engineer

What You'll Do

Lead Runtime Design & Development:

• Own the core runtime architecture supporting AI training and inference at scale.
• Design resilient and elastic runtime features (e.g. dynamic node scaling, job recovery) within our custom PyTorch stack.
• Optimize distributed training reliability, orchestration, and job-level fault tolerance.

Drive Performance at Scale:

• Profile and enhance low-level system performance across training and inference pipelines.
• Improve packaging, deployment, and integration of customer models in production environments.
• Ensure consistent throughput, latency, and reliability metrics across multi-node, multi-GPU setups.

Build Internal Tooling & Frameworks:

• Design and maintain libraries and services that support model lifecycle: training, checkpointing, fault recovery, packaging, and deployment.
• Implement observability hooks, diagnostics, and resilience mechanisms for deep learning workloads.
• Champion best practices in CI/CD, testing, and software quality across the AI Runtime stack.

Collaborate & Mentor:

• Work cross-functionally with Research, Infrastructure, and Product teams to align runtime development with customer and platform needs.
• Guide technical discussions, mentor junior engineers, and help scale the AI Runtime team’s capabilities.

What You’ll Need to Be Successful

• 8+ years of experience in systems/software engineering, with deep exposure to AI runtime, distributed systems, or compiler/runtime interaction.
• Experience in delivering PaaS services.
• Proven experience optimizing and scaling deep learning runtimes (e.g. PyTorch, TensorFlow, JAX) for large-scale training and/or inference.
• Strong programming skills in Python and C++ (Go or Rust is a plus).
• Familiarity with distributed training frameworks, low-level performance tuning, and resource orchestration.
• Experience working with multi-GPU, multi-node, or cloud-native AI workloads.
• Solid understanding of containerized workloads, job scheduling, and failure recovery in production environments.

Nice to Have

• Contributions to PyTorch internals or open-source DL infrastructure projects.
• Familiarity with LLM training pipelines, checkpointing, or elastic training orchestration.
• Experience with Kubernetes, Ray, TorchElastic, or custom AI job orchestrators.
• Background in systems research, compilers, or runtime architecture for HPC or ML.
• Startup previous experience