Senior Machine Learning Researcher, On-Device Optimization

Hp Iq•Palo Alto, CA

10d

About The Position

As a Machine Learning Researcher, you’ll focus on advancing the state-of-the-art in on-device AI optimization. This role bridges applied research and product development, with a heavy focus on techniques like quantization, pruning, and efficient model representation. You’ll bring academic expertise into real-world systems that power intelligent assistants running directly on HP laptops and edge devices.

Requirements

2+ years of industry or applied research experience
Strong background in model optimization for edge computing or mobile/embedded deployment
Familiarity with PyTorch, ONNX, TensorRT, OpenVINO, QNN, or Llama.cpp
Understanding of tradeoffs in asymmetric/symmetric quantization, calibration methods, and inference tuning

Nice To Haves

PhD in Computer Science, Electrical Engineering, or related field with focus on efficient ML, systems ML, or compiler design for ML
Experience publishing at top ML/Systems conferences (e.g., NeurIPS, ICML, MLSys)
Familiarity with embedded ML for consumer devices
GPU and system-level profiling tools (e.g., CUDA, nvprof, perf)
Contributions to open-source ML optimization frameworks

Responsibilities

Research and implement model compression techniques including quantization, low-rank factorization, distillation, and pruning
Develop methods to deploy SOTA transformer and vision models on-device under hardware constraints
Lead investigations into hardware-aware training strategies to optimize latency, throughput, and memory usage
Collaborate with software engineers and system architects to integrate models into AI companion apps
Evaluate and benchmark different frameworks and quantization strategies (e.g., AWQ, GPTQ, SmoothQuant)

Benefits

Health insurance
Dental insurance
Vision insurance
Long term/short term disability insurance
Employee assistance program
Flexible spending account
Life insurance
Generous time off policies, including; 4-12 weeks fully paid parental leave based on tenure
11 paid holidays
Additional flexible paid vacation and sick leave