Principal Engineer responsible for enhancing service integrations at CDP Global, focusing on environmental impact. Collaborate with tech leads to align on integration standards and document architecture.
About the role
- Principal Software Engineer focusing on AI Inference, optimizing GPU performance and contributing to open-source projects. Collaborative role improving inference systems like vLLM and SGLang at NVIDIA.
Responsibilities
- Drive upstream-first engineering in vLLM/SGLang: author and land PRs or equivalent experience, engage in development discussions, help compose roadmaps, and build durable maintainer relationships.
- Build and implement inference-runtime features that improve efficiency, latency, and tail behavior: request scheduling, batching policies, KV-cache management (paging/sharding), memory planning, and streaming.
- Optimize core hot paths across the stack—from Python orchestration down to C++/CUDA kernels—using profiling and measurement to guide decisions.
- Improve multi-GPU and multi-node inference: communication patterns, parallelism strategies (tensor/sequence/pipeline), and system-level scaling/efficiency.
- Strengthen correctness, robustness, and operability: determinism where needed, graceful degradation, backpressure, observability hooks, and performance regression testing.
- Collaborate across NVIDIA to integrate upstream advances with production needs (deployment patterns, compatibility, security posture) while keeping changes broadly adoptable by the community.
- Mentor senior engineers, raise the technical bar through build reviews, and establish guidelines for performance engineering and upstream contribution workflows.
Requirements
- 15+ years building production software with significant depth in systems engineering
- strong track record of owning ambiguous, high-impact technical problems end-to-end
- demonstrated expertise in LLM inference/serving systems (e.g., vLLM, SGLang) and the tradeoffs that drive real production performance
- strong programming skills in Rust, C++, Python, CUDA; ability to read, modify, and optimize performance-critical code across layers
- experience with GPU performance analysis tools and methodologies (profiling, microbenchmarking, memory/comms analysis) and a strong measurement culture
- solid foundation in distributed systems and concurrency: queues/schedulers, RPC/streaming, multi-process/multi-threaded runtime behavior, and scaling patterns across nodes
- excellent communication skills; ability to influence across teams and represent NVIDIA well in open-source technical forums
- BS/MS in Computer Science, Computer Engineering, or related field (or equivalent experience)
Benefits
- equity
- benefits
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