Senior AI Software Engineer - Autonomous Systems

Advanced Micro Devices, Inc•San Jose, CA

2d•Hybrid

About The Position

We are seeking a highly skilled Senior AI Software Engineer to join our team. The ideal candidate will be responsible for designing and implementing AI-driven software solutions tailored for autonomous systems. This role requires deep expertise in AI frameworks, model analysis and optimization, GPU-accelerated computing, and system performance profiling within high-performance autonomous environments.

Requirements

AI Frameworks: Proven experience designing and implementing solutions using leading AI frameworks such as PyTorch, TensorFlow, JAX, or ONNX Runtime, with a focus on autonomous applications.
AI Model Analysis & Optimization: Strong knowledge and hands-on experience with model profiling, benchmarking, quantization, pruning, and distillation techniques to optimize AI models for performance and efficiency.
ROCm, CUDA & GPU Computing: Deep expertise in ROCm or CUDA programming, GPU kernel optimization, and GPU memory management for accelerating AI inference and training workloads.
System Performance Analysis: Expertise in profiling and analyzing end-to-end AI system performance using tools such as NVIDIA Nsight, TensorRT, Triton Inference Server, or similar profiling and optimization platforms.
Autonomous Systems: Experience deploying AI models within software stacks, including integration with ROS/ROS2, real-time systems, and edge AI hardware platforms (NVIDIA Jetson, etc.).
Problem-Solving Skills: Excellent problem-solving skills and attention to detail in debugging complex AI model behavior, performance regressions, and hardware-software interactions.
Collaboration and Communication: Ability to work collaboratively in a cross-functional team environment with strong written and verbal communication skills.
Bachelor’s or Master’s in Electrical Engineer, Computer Engineering, Computer Science, or a closely related field

Responsibilities

Select and optimize AI models for autonomous applications, ensuring scalability, low latency, and real-time performance across edge and cloud deployments.
Collaborate with cross-functional teams to ensure seamless integration of AI frameworks into software stacks, optimizing inference pipelines and model performance for real-world use cases.
Provide technical leadership and guidance in AI software best practices, focusing on deep learning frameworks, GPU-accelerated computing, model compression, and efficient deployment strategies for autonomous systems.
Analyze and optimize AI software stack performance, particularly in real-time inference, GPU-accelerated, and autonomous navigation environments, identifying bottlenecks and implementing targeted improvements.
Stay updated with the latest trends and technologies in AI frameworks, foundation models, edge AI deployment and autonomous systems integration, offering insights and recommendations to continuously advance AI capabilities.