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# Software Engineer, Edge Systems & Runtime (C++), Materra

- **Company:** Google LLC
- **Location:** Mountain View, CA, United States
- **Experience:** Experienced
- **Salary:** $166,000.0 - $244,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), C++ (Programming Language), Profiling, Nvidia CUDA, Computer Engineering, Memory Management, Hardware Design, Python (Programming Language), Machine Learning, Rapid Prototyping Process, Real-Time Operating Systems, Data Streaming, Systems Integration, Video Editing, Graphics Processing Unit (GPU), Deep Learning, Information Technology, ONNX (Open Neural Network Exchange) Format, TensorRT, Multiaccess Edge Computing, C++14
- **Published:** July 30, 2026
- **Apply:** https://dejobs.org/x/x/EC3063C606474F439CA3E5C278C5BA49/job/

## About the Role

* Education: Degree in Computer Science, Computer Engineering, Electrical Engineering, Robotics, or a related technical field.
 * Modern C++ Proficiency: 3+ years expertise in modern C++ (C++17/20), including multithreading, asynchronous I/O, IPC, and manual/smart memory management.
 * On-Device ML Deployment: 3+ years hands-on experience profiling, compiling, and optimizing deep learning models for edge hardware using TensorRT, CUDA, ONNX, or similar acceleration frameworks.
 * Systems Optimization: Proven experience navigating, profiling, and optimizing C++ execution paths under strict, low-latency performance constraints.

Preferred Skills

 * Sensor & Hardware Integration: Familiarity with hardware communication APIs, SDKs, or sensor streams (e.g., GigE Vision, GenICam, frame-grabbers, or point clouds).
 * Domain Experience: 3+ years exposure to real-time perception systems in robotics, industrial automation, autonomous vehicles, or video processing engines.
 * Scripting & Automation: Proficiency in Python for rapid prototyping and system integration.
 * Advanced Acceleration: Experience writing custom CUDA kernels, custom TensorRT plugins, or working with real-time OS extensions.

## Description

to join our team, focusing on high-performance C++ software and on-device machine learning execution. In this role, you will work on low-latency software pipelines that process multi-sensor data streams and execute deep learning models on edge GPUs.

Your primary focus will be optimizing our production C++ runtime infrastructure, refining memory usage, improving data streaming between host and device, and tuning execution engines to squeeze out low-latency optimizations within single-digit milliseconds., * Profile and optimize established C++ software pipelines and GPU runtime engines to reduce latency and maximize hardware utilization.

 * Refine host-to-device streaming pipelines using efficient memory management patterns (e.g., pinned memory, zero-copy buffers, lock-free queues).
 * Deploy, update, and optimize deep learning model execution runtimes (e.g., TensorRT, CUDA, ONNX Runtime) on edge GPUs.
 * Enhance asynchronous C++ data streams routing multi-sensor inputs into model inference loops.
 * Collaborate with machine learning engineers to ensure smooth handoffs from training pipelines to production C++ runtimes.


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