Drive CI and infrastructure capabilities that make deep learning compiler development fast, reliable, and scalable.
This includes improving signal-to-noise (flake reduction, reproducibility, and richer diagnostics), accelerating iteration cycles, scaling capacity and coverage across models/hardware/software configurations, and building strong observability (metrics, logging, tracing, dashboards) so failures are easy to understand and fix.
Explore practical uses of AI to enhance CI workflows—such as smarter test selection, automated triage/summarization, and faster issue isolation—ultimately increasing the quality and speed of deep learning compiler development, testing, and release.

Requirements

BS, MS, or PhD (or equivalent experience) in Computer Science, Computer/Electrical Engineering, Mathematics, or related field
3+ years of professional experience designing and scaling CI/CD, build/release, or developer productivity infrastructure for DL/GPU software environments
Strong software engineering skills (Python required) with ability to architect, implement, and debug complex systems end-to-end
Hands-on experience building CI/MLOps platform capabilities—pipeline orchestration, artifact/package management, and production-grade observability (logs/metrics/dashboards)—with strong reliability and maintainability
Experience with deep learning frameworks/runtime stacks (e.g., PyTorch, JAX, vLLM, SGLang, TensorRT, NeMo) and running real workloads in production-like environments
Working knowledge of Linux-based development and debugging across complex software/hardware stacks (drivers, CUDA libraries, containers, cluster schedulers, etc.)

Benefits

equity
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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonCI/CDMLOpspipeline orchestrationartifact managementobservabilitydeep learning frameworksCUDAdebuggingscalable systems

Soft Skills

problem-solvingcommunicationcollaborationanalytical thinkingattention to detail