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Qdrant Jobs in California (NOW HIRING)

Use Qdrant to find high-relevance candidates for re-entry carousels based on session history and global trends. * Experiment Lifecycle: Use MLFlow to manage, track, and deploy experiments, ensuring a ...

Senior AI Engineer

Los Angeles, CA · On-site

$112.60K - $154.60K/yr

Ingest from BigQuery, object-store lakes (Parquet, Avro); generate embeddings and persist to vector DBs (Qdrant/PgVector); enforce governance via OpenMetadata and column-level ACLs. * Scalable ...

Data Engineer (Founding Team)

Bodega Bay, CA · On-site

$135.90K - $163.20K/yr

Weaviate, Qdrant, Pinecone) and embedding pipelines * Experience building or contributing to enterprise connector ecosystems * Knowledge of ontology versioning , graph diffing , or semantic schema ...

Senior AI Engineer

Los Angeles, CA

$112.60K - $154.60K/yr

Data & Storage Architecture:  Ingest from BigQuery, object-store lakes (Parquet, Avro); generate embeddings and persist to vector DBs (Qdrant/PgVector); enforce governance via OpenMetadata and ...

DevOps Engineer (Founding Team)

Bodega Bay, CA · On-site

$62.50 - $85.75/hr

Familiarity with vector DBs (Weaviate, Qdrant, Pinecone) and embedding pipelines * Monitoring and governing long-running or multi-agent chains * Auditability and replay systems for agent decision ...

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Qdrant information

How does Qdrant compare to Pinecone?

Qdrant and Pinecone are both vector similarity search platforms used in AI and machine learning applications. Qdrant is open-source and offers flexible deployment options, while Pinecone is a managed service with a focus on scalability and ease of use. Both require knowledge of vector search concepts and can be integrated with various data processing tools.

What is the difference between Qdrant vs Data Scientist?

AspectQdrantData Scientist
Required CredentialsTechnical certifications, knowledge of vector databasesDegree in Data Science, Statistics, or related field
Work EnvironmentTech companies, startups, AI-focused firmsResearch labs, tech companies, consulting firms
Industry UsageAI, machine learning, data storageData analysis, predictive modeling, research

Qdrant primarily focuses on managing and deploying vector similarity search databases, requiring technical skills in database management and AI tools. Data Scientists analyze data, build models, and interpret results. While both roles operate within the tech and AI industry, Qdrant specialists are more technical and infrastructure-oriented, whereas Data Scientists focus on data analysis and modeling.

What cities in California are hiring for Qdrant jobs? Cities in California with the most Qdrant job openings:
Infographic showing various Qdrant job openings in California as of May 2026, with employment types broken down into 96% Full Time, and 4% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution.

Senior Machine Learning Engineer, Recommender Systems

HP IQ

Palo Alto, CA • On-site

$150K - $250K/yr

Other

Posted 7 days ago


Job description

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