HP Inc.
Senior Machine Learning Researcher, On-Device Optimization
HP Inc., Palo Alto, California, United States, 94306
Who We Are
HP IQ is HP's new AI innovation lab. Combining startup agility with HP's global scale, we're building intelligent technologies that redefine how the world works, creates, and collaborates.
We're assembling a diverse, world-class team-engineers, designers, researchers, and product minds-focused on creating an intelligent ecosystem across HP's portfolio. Together, we're developing intuitive, adaptive solutions that spark creativity, boost productivity, and make collaboration seamless.
We create breakthrough solutions that make complex tasks feel effortless, teamwork more natural, and ideas more impactful-always with a human-centric mindset.
By embedding AI advancements into every HP product and service, we're expanding what's possible for individuals, organisations, and the future of work.
Join us as we reinvent work, so people everywhere can do their best work.
About The Role
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.
What You Might Do
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)
Essential Qualifications PhD in Computer Science, Electrical Engineering, or related field with focus on efficient ML, systems ML, or compiler design for ML
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
Preferred Skills 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
The pay range for this role is
$150,000
to
$250,000
USD annually with additional opportunities for pay in the form of bonus and/or equity (applies to United States of America candidates only). Pay varies by work location, job-related knowledge, skills, and experience. Benefits: HP offers a comprehensive benefits package for this position, including: 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 (US benefits overview (https://hpbenefits.ce.alight.com/) )
The compensation and benefits information is accurate as of the date of this posting. The Company reserves the right to modify this information at any time, with or without notice, subject to applicable law. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
#J-18808-Ljbffr
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)
Essential Qualifications PhD in Computer Science, Electrical Engineering, or related field with focus on efficient ML, systems ML, or compiler design for ML
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
Preferred Skills 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
The pay range for this role is
$150,000
to
$250,000
USD annually with additional opportunities for pay in the form of bonus and/or equity (applies to United States of America candidates only). Pay varies by work location, job-related knowledge, skills, and experience. Benefits: HP offers a comprehensive benefits package for this position, including: 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 (US benefits overview (https://hpbenefits.ce.alight.com/) )
The compensation and benefits information is accurate as of the date of this posting. The Company reserves the right to modify this information at any time, with or without notice, subject to applicable law. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
#J-18808-Ljbffr