Advisory Software Engineer

***This is a hybrid role, on site in our Morrisville, NC office at least 3 days per week. ***

Description:

Lenovo Research is looking for an experienced Software Engineer to join our On-Device Intelligence team within the broader PC Innovation and Ecosystem organization. This US-based team develops software and runtime capabilities that enable AI models to run efficiently and reliably on Lenovo PCs by combining on-device inference, internal PC sensor signals, and intelligent user-context algorithms. In this role, you will design, implement, optimize, and debug production-quality software for on-device AI model deployment and runtime execution. You will work closely with researchers, platform engineers, and product teams to integrate AI models into Windows-based PC experiences, improve latency, memory usage, power efficiency, and system reliability, and turn research concepts into robust prototypes, demos, and product-ready components.

What You'll Do

In this role, you will design, implement, optimize, and debug production-quality software for on-device AI model deployment and runtime execution. You will work closely with researchers, platform engineers, and product teams to integrate AI models into Windows-based PC experiences, improve latency, memory usage, power efficiency, and system reliability, and turn research concepts into robust prototypes, demos, and product-ready components.

This is a hybrid role, on site in our Morrisville, NC office at least 3 days per week.

BasicQualifications:

• BS or MS degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field
• 5+ years of professional software development experience, preferably building Windows applications, system components, SDKs, services, or runtime software
• Strong programming skills in C++ and Python, with solid understanding of data structures, algorithms, debugging, multithreading, optimization, and design
• Hands-on experience deploying, integrating, or optimizing AI/ML models for on-device or edge inference environments
• Experience working with AI inference runtimes or model deployment frameworks such as ONNX Runtime, TFLite, PyTorch Mobile, DirectML, OpenVINO, RyzenAI or similar technologies
• Understanding of model deployment tradeoffs including latency, memory footprint, power consumption, accuracy, hardware acceleration, and runtime stability

PreferredQualifications:

• Experience building AI runtime components, inference pipelines, model loading/execution flows, or abstraction layers for multiple model formats and hardware backends
• Experience with model optimization techniques such as quantization, pruning, distillation, operator fusion, batching, caching, or hardware-specific acceleration
• Familiarity with PC, edge, or embedded hardware acceleration technologies such as CPU, GPU, NPU, DSP, DirectML, CUDA, OpenCL, or vendor-specific AI accelerators
• Experience integrating AI features into Windows applications, background services, device software, or user-facing PC experiences
• Strong engineering practices, including Git, unit testing, profiling, CI/CD, code review, technical documentation, and cross-functional collaboration