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# Staff Software Engineer, Inference Performance Optimization, GenAI, DeepMind

- **Company:** Google LLC
- **Location:** Mountain View, CA, United States
- **Experience:** Expert
- **Salary:** $207,000.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Profiling, Computer Engineering, Software Debugging, Distributed Systems, Python (Programming Language), Open Source Technology, Performance Tuning, Software Engineering, Pytorch, Large Language Models, Information Technology, Codebase, TensorRT
- **Published:** August 21, 2026
- **Apply:** https://dejobs.org/x/x/F152BC85580F4EE19478866B219E4D63/job/

## About the Role

* Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, or a related technical field, or equivalent practical experience.
 * 8 years of experience in software development.
 * Experience in Python and C++, including navigating, debugging, and modifying serving codebases.
 * Experience with AI model execution constraints, throughput-latency tradeoffs, memory bandwidth limitations, and modern serving architectures., * Experience with real world LLM inference serving environments or direct contributions to modern open-source inference frameworks (e.g., vLLM, TensorRT-LLM, SGLang, Dynamo).
 * Experience profiling workloads using standard ML profilers (e.g., PyTorch profiler) and internal trace analysis tools.
 * Experience with observability and reliability for large distributed systems.
 * Familiarity with GPU/TPU/accelerator performance concepts (e.g. memory bandwidth, quantization, collective communication, kernel), and can reason their implications to the overall inference serving performance.

## Description

* Analyze and optimize AI inference workloads across the application, model, and distributed fleet infrastructure layers to methodically increase throughput-per-GPU and reduce latency.
 * Design and implement inference optimization techniques.
 * Investigate and resolve complex model inference performance bottlenecks across the stack.
 * Model the latency-to-cost impacts of system variables (such as batch-sizing and utilization goals) and translate these insights into actionable signals that drive production systems.
 * Develop investigative tools and metrics (e.g., compute/FLOPs funnels) that track where compute is spent across the fleet.


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