ZipRecruiter

Log In

1

Recommender Systems Jobs (NOW HIRING)

next page

Showing results 1-20

People also search for

All JobsRecommender Systems Jobs

Recommender Systems information

See salary details

$46K

$112K

$197K

How much do recommender systems jobs pay per year?

As of May 29, 2026, the average yearly pay for recommender systems in the United States is $111,995.00, according to ZipRecruiter salary data. Most workers in this role earn between $71,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What is a Recommender Systems job?

A Recommender Systems job involves designing, building, and optimizing algorithms that suggest relevant content, products, or services to users based on their preferences and behavior. Professionals in this field work with machine learning, data science, and engineering to develop personalized recommendations for platforms like e-commerce sites, streaming services, and social media. They analyze large datasets, fine-tune models, and collaborate with cross-functional teams to improve user experiences and drive business goals.

What are the key skills and qualifications needed to thrive in the Recommender Systems position, and why are they important?

To thrive in a Recommender Systems role, you need a strong background in computer science, machine learning, statistics, and data analysis, often supported by a relevant degree or equivalent experience. Familiarity with programming languages such as Python or Scala, frameworks like TensorFlow or PyTorch, and experience with big data tools and collaborative filtering algorithms are typically required. Excellent problem-solving abilities, communication skills, and the capacity to work collaboratively with cross-functional teams are invaluable soft skills. These competencies are vital to designing effective recommendation algorithms that enhance user experiences and deliver business value.

What are the common daily responsibilities for someone working in Recommender Systems?

Professionals in Recommender Systems typically spend their days designing, developing, and optimizing algorithms that suggest personalized content or products to users. Their tasks often involve analyzing large datasets, implementing and testing machine learning models, and collaborating closely with engineers, data scientists, and product managers to deploy these solutions. Regularly reviewing user feedback and system metrics is also important for continuous improvement. The role often requires balancing technical work with cross-team communication to ensure the recommended systems align with overall business objectives.
More about Recommender Systems jobs
What are the most commonly searched types of Recommender Systems jobs? The most popular types of Recommender Systems jobs are:
What states have the most Recommender Systems jobs? States with the most job openings for Recommender Systems jobs include:
What job categories do people searching Recommender Systems jobs look for? The top searched job categories for Recommender Systems jobs are:
Recommender Systems jobs near you
Infographic showing various Recommender Systems job openings in the United States as of May 2026, with employment types broken down into 1% As Needed, 94% Full Time, and 5% Contract. Highlights an 89% Physical, 3% Hybrid, and 8% Remote job distribution, with an average salary of $111,995 per year, or $53.8 per hour.

Lead Machine Learning Engineer, Recommender Systems

HP IQ

Palo Alto, CA

$175K - $275K/yr

Other

Posted 8 days ago

Job description

About The Role 

As a Lead 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 
  • Drive the design, implementation, and scaling of recommendation and retrieval algorithms for our AI Companion app
  • Set the technical vision for 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
  • Partner with cross-functional leaders in ML, infrastructure, and product teams to deploy fast and efficient RAG workflows
  • Build and maintain retrieval indexes optimized for latency and memory 
  • Mentor and guide engineers across the team, fostering best practices in experimentation, model evaluation, and production deployment.
Essential Qualifications 
  • 8+ years of software development experience with exposure to ML engineering
  • Deep expertise in recommender systems, embeddings, and ranking models
  • Proven experience building or scaling document search or retrieval systems
  • Strong understanding of 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:  $175,000 - $275,000

Apply