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 Engineer – Recommender Systems, you’ll play a central role in improving HP’s Retrieval-Augmented Generation (RAG) pipelines for private and local data. You’ll build intelligent, context-aware retrieval systems that enhance user interactions with documents, meetings, and applications—all on-device. This role blends deep ML experience with product-focused engineering.What You Might Do

• Design, implement, and scale recommendation and retrieval algorithms for our AI Companion app
• Improve vector search and similarity matching models to identify relevant documents across structured and unstructured data
• Analyze user interactions and system performance to guide algorithmic improvements
• Work across ML, infrastructure, and product teams to deploy fast and efficient RAG workflows
• Build and maintain retrieval indexes optimized for latency and memory

Essential Qualifications

• 7+ years of software development experience with exposure to ML engineering
• Strong foundation in recommender systems, embeddings, and ranking models
• Experience building or scaling document search or retrieval systems
• Familiarity with vector databases (e.g., FAISS, Pinecone, Qdrant)
• Proficient in Python and one systems language (e.g., C++, Java)

Preferred Skills

• Background in LLM integration or fine-tuning for RAG workflows
• Industry experience at companies like Google (Search, YouTube), Meta (Feed, Ads), or Twitter (Timeline, Trends)
• Experience with ML pipeline tools (Airflow, Ray, TorchServe)
• Previous experience improving search relevance, click-through rate, or long-term engagement

Salary Range: $150,000 - $250,000

DISCLAIMER

• This job description describes the general nature and level of work performed in this role. It is not intended to be an exhaustive list of all duties, skills, responsibilities, knowledge, etc. These may be subject to change and additional functions may be assigned as needed by management.

• Design, implement, and scale recommendation and retrieval algorithms for our AI Companion app
• Improve vector search and similarity matching models to identify relevant documents across structured and unstructured data
• Analyze user interactions and system performance to guide algorithmic improvements
• Work across ML, infrastructure, and product teams to deploy fast and efficient RAG workflows
• Build and maintain retrieval indexes optimized for latency and memor