Description :

Role & Responsibilities :

As Lead/Staff AI Runtime Engineer, youll 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).

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.

Youll 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.

What youll 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, check pointing, 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 teams capabilities.

Ideal Candidate :

- 5+ 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 inproduction environments.