Senior Machine Learning Engineer Fine-Tuning and On-device AI

Senior Machine Learning Engineer - Fine-Tuning and On-device

AI HP IQ

Palo Alto, CA Job Details Full-time $120,000 - $215,000 a year 11 hours ago Benefits Paid parental leave Paid holidays Disability insurance Health insurance Dental insurance Flexible spending account Parental leave Employee assistance program Vision insurance Life insurance Qualifications Stakeholder engagement 7 years Data transformation pipeline development PyTorch Quantization Model deployment Experimental design Machine learning libraries Senior level AI Machine learning frameworks Full Job Description 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 We are seeking a Senior Machine Learning Engineer to lead the fine-tuning, optimization, and deployment of AI models for diverse tasks, with a strong emphasis on on-device inference. You will work on cutting-edge applications such as orchestration, planning, multi-agent coordination, and other intelligent decision-making systems. You will be responsible for adapting foundation models (LLMs, multimodal models) to specialized domains, making them fast, accurate, and efficient for resource-constrained environments—while ensuring robustness and safety. What You Might Do Model Fine-Tuning & Adaptation Fine-tune large language models, multimodal models, and task-specific models for orchestration, planning, and any other workflows as defined. Design and run experiments to improve task accuracy, robustness, and generalization. Explore and apply methods like full fine-tuning, LoRA, QLoRA and other types of parameter-efficient fine-tuning. Employee advanced techniques such as

QAT, DPO, GRPO

to further improve the model quality. On-Device Optimization Prune, quantize and compress models (e.g., INT8, INT4, mixed-precision) for CPU, GPU, NPU and edge accelerators. Optimize models for low-latency inference using frameworks like Open

VINO, ONNX

Runtime, QNN etc.. Data Pipeline & Deployment Build robust data pipelines for domain-specific datasets, including synthetic data generation and annotation. Define evaluation metrics. Perform evaluations and analyze results. Establish best practices for versioning, reproducibility, and continuous improvement of model performance. AI Orchestration & Planning Develop and refine models to support multi-step reasoning, tool orchestration, and decision planning. Work with stakeholders on orchestrator architecture. Collaborate with product and research teams to design intelligent, context-aware assistant capabilities. Essential Qualifications 7+ years of experience in applied machine learning, including at least 3 years in LLM fine-tuning. Proficiency in Python and ML frameworks ecosystem (HuggingFace, PyTorch). Strong understanding of transformer architectures, attention mechanisms, and PEFT techniques. Experience with on-device inference optimization (Open

VINO, ONNX, QNN

). Familiarity with orchestration/planning architectures and techniques for AI assistants. Track record of delivering production-ready ML solutions in latency-sensitive environments. Preferred Qualifications Experience with multi-agent systems or AI assistant orchestration. Familiarity with advanced inference optimization techniques such as KV cache paging , flash attention. Knowledge about common inference engines, including but not limited to llama.cpp, vLLM.

Salary Range:

$120,000 - $215,000 Compensation & Benefits (Full-Time Employees) The salary range for this role is listed above. Final salary offered is based upon multiple factors including individual job-related qualifications, education, experience, knowledge and skills. At HP IQ, we offer a competitive and comprehensive benefits package, 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 Why HP IQ? HP IQ is HP's new AI innovation lab, building the intelligence to empower humanity—reimagining how we work, create, and connect to shape the future of work. Innovative Work Help shape the future of intelligent computing and workplace transformation. Autonomy and Agility Work with the speed and focus of a startup, backed by HP's scale. Meaningful Impact Build AI-powered solutions that help people and organisations thrive. Flexible Work Environment Freedom and flexibility to do your best work. Forward-Thinking Culture We learn fast, stay future-focused, and imagine what comes next—together. Equal Opportunity Employer (EEO) Statement HP, Inc. provides equal employment opportunity to all employees and prospective employees, without regard to race, color, religion, sex, national origin, ancestry, citizenship, sexual orientation, age, disability, or status as a protected veteran, marital status, familial status, physical or mental disability, medical condition, pregnancy, genetic predisposition or carrier status, uniformed service status, political affiliation or any other characteristic protected by applicable national, federal, state, and local law(s). Please be assured that you will not be subject to any adverse treatment if you choose to disclose the information requested. This information is provided voluntarily. The information obtained will be kept in strict confidence.