Rahul Anand Sharma

I am an ML Systems Engineer & Researcher at Google Cloud, specializing in hardware-software co-design, distributed accelerator systems, and high-performance inference.

At Google Cloud, I focus on inference optimization of Gemini models across GPUs and TPUs, and previously drove accelerator enablement and scaling analyses for TPU v7 (Ironwood) (Google Cloud Tech Impact Award, 2026).

Earlier, I was an Applied Scientist at AWS architecting GenAI root-cause analysis for Apache Spark at hyperscale (Amazon Q), completed my Ph.D. in Electrical & Computer Engineering at Carnegie Mellon University (CyLab, CIT Dean’s Fellow), and conducted systems research at Microsoft Research (FarmBeats, DeepEdge).

My work spans 15+ publications in top systems and security venues (SIGCOMM, NSDI, USENIX Security), 2 US patents, and systems recognized by GatesNotes, Satya Nadella, and the front page of Hacker News.

---

Core Focus

• LLM Inference Optimization: Gemini model serving across GPUs and TPUs, KV-cache optimization, model parallelism, and low-latency runtimes.
• Accelerator Infrastructure & Scaling: TPU v7 (Ironwood) cluster scaling, collective communication, and interconnect bottleneck modeling.
• Systems & Network Security: Active network testing at hyperscale, ML-driven telemetry, and wireless sensing.