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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, Discretization, Information Technology, SGLang, Codebase, TensorRT, Model Inference, DeepMind
- **Published:** October 3, 2026
- **Apply:** https://dejobs.org/x/x/E98D2D14D3424D69BE3A435B3E014064/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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