About the job
We are now looking for a Senior System Software Engineer to work on Dynamo. NVIDIA is hiring software engineers for its GPU-accelerated deep learning software team. Academic and commercial groups around the world are using GPUs to power a revolution in AI, enabling breakthroughs in problems from image classification to speech recognition to natural language processing. We are a fast-paced team building Generative AI inference platform to make design and deployment of new AI models easier and accessible to all users.
Responsibilities
Contribute to the development of disaggregated serving for Dynamo-supported inference engines (vLLM, SGLang, TRT-LLM) and expand to support multi-modal models for embedding disaggregation.
Innovate in the management and transfer of large KV caches across heterogeneous memory and storage hierarchies, using the NVIDIA Optimized Transfer Library (NIXL) for low-latency, cost-effective data movement.
Build new features to the Dynamo Rust Runtime Core Library and design, implement, and optimize distributed inference components in Rust and Python.
Balance a variety of objectives: build robust, scalable, high performance software components to support our distributed inference workloads; work with team leads to prioritize features and capabilities; load-balance asynchronous requests across available resources; optimize prediction throughput under latency constraints; and integrate the latest open source technology.
Qualifications
Minimum
Masters or PhD or equivalent experience
3+ years in Computer Science, Computer Engineering, or related field
Ability to work in a fast-paced, agile team environment
Excellent Rust/Python/C++ programming and software design skills, including debugging, performance analysis, and test design.
Experience with high scale distributed systems and ML systems
Preferred
Prior contributions to open-source AI inference frameworks (e.g., vLLM, TensorRT-LLM, SGLang).
Experience with GPU memory management, cache management, or high-performance networking.
Understanding of LLM-specific inference challenges, such as context window scaling and multi-model agentic and reasoning workflows.
Prior experience with disaggregated serving and multi modal models (Vision-Language models, Audio Language Models, Video Language Models)