Systems Research Engineer

• Architect and implement distributed system components for AI workloads across CPU, GPU, and NPU clusters.
• Conduct in-depth profiling and performance tuning of inference pipelines, focusing on KV cache management.
• Develop low-latency, fault-tolerant AI serving frameworks using vLLM, Ray Serve, and PyTorch Distributed.
• Research and prototype novel techniques for cache sharing, data locality, and resource orchestration.
• Translate innovative designs into publishable contributions at top-tier venues (e.g., OSDI, NSDI, MLSys).
• Collaborate with global research teams to drive the internal adoption of novel system architectures.

• Preferably a PhD, at minimum a masters degree, in Computer Science, distributed systems, or related field.
• Strong knowledge of Distributed Systems, OS internals, and Machine Learning systems architecture.
• Hands-on experience with LLM serving frameworks (vLLM, Ray Serve, TensorRT-LLM, or TGI).
• Proficiency in C/C++ for systems development and Python for research prototyping.
• Solid grounding in distributed algorithms, load balancing, and state management.
• Proven ability to conduct systems research, ideally evidenced by publications in top-tier conferences.

Keywords: Systems Research Engineer / Distributed Systems / AI Infrastructure / LLM Serving / vLLM / KV Cache / Performance Optimization / Machine Learning Systems / C++ / Python / Ray Serve / PyTorch Distributed / Cloud Infrastructure

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