Model Optimization Engineer

About Our Team

Lenovo is building Quantum, a next-generation hybrid AI platform that spans Windows, Android, and cloud. We are hiring for a Model Optimization Engineer to optimize and deploy large models for edge devices. In this role you will master multiple technologies, such as, Quantization frameworks (TensorRT, ONNX Runtime), edge AI runtimes (ExecuTorch, llama.cpp), NPU SDKs (Qualcomm QNN), model compression libraries, profiling tools (NVIDIA Nsight, Snapdragon Profiler)

On-device AI is the future-enabling real-time, private, and always-available intelligence. You'll push the boundaries of what's possible on mobile hardware, delivering AI experiences that run locally with low latency and all-day battery life. Your optimizations directly enable breakthrough product features.

Location: Chicago, IL,

Hybrid (3 days on-site, 2 days remote)

What You'll Do

• Optimization and deployment of large models (LLMs, VLMs, diffusion) for edge devices using quantization (INT4/INT8), pruning, knowledge distillation, and LoRA
• Partner with silicon teams to optimize model execution on heterogeneous hardware: NPUs (Qualcomm Hexagon, Google Edge TPU), GPUs, and CPUs
• Implement and benchmark deployment frameworks: TensorRT-LLM, ONNX Runtime, ExecuTorch, llama.cpp, MLC-LLM
• Drive hardware-software co-design, influencing sensor and silicon roadmaps to enable efficient AI inference
• Build ML ops infrastructure: model serving, A/B testing, performance monitoring, continuous optimization
• Stay at the forefront of on-device AI: sub-10B parameter models, mixed precision, sparse attention, federated learning

Basic Qualifications

• 3+ years in ML engineering or systems, with 3+ years focused on model optimization and deployment
• Bachelor's degree in an Engineering or Computer Science.
• Experience in model compression: quantization (QAT, PTQ), pruning, distillation, low-rank adaptation
• Hands-on experience with mobile/edge AI frameworks (TensorRT, ONNX, TFLite, CoreML)
• Experience in C++/Python and performance optimization (CUDA, OpenCL, or NPU programming)
• Experience in shipping ML models to production on resource-constrained devices.

• Understanding of hardware architectures: NPU/GPU/CPU characteristics, SIMD operations, memory hierarchies

The base salary budgeted range for this position is $110K-$150K. Individuals may also be considered for bonus and/or commission.