Lead/Staff AI Runtime, FlexAI

• We are seeking a skilled and experienced Lead/Staff AI Runtime Engineer
• This is a hands-on leadership role - perfect for a systems-minded software engineer who thrives at the intersection of AI workloads, runtimes, and performance-critical infrastructure
• 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

Desirable

• At FlexAI, we’re building a high-performance, cloud-agnostic AI compute platform designed for next-generation training and inference workloads
• As Lead/Staff AI Runtime Engineer, you’ll play a pivotal role in the design, development, and optimization of the core runtime infrastructure that powers distributed training and deployment of large AI models (LLMs and beyond)
• You’ll own critical components of our PyTorch-based stack, lead technical direction, and collaborate across engineering, research, and product to push the boundaries of elastic, fault-tolerant, high-performance model execution
• 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
• 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
• 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
• 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

• We usually respond within three days