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