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How much do recommender systems jobs pay per year?

As of Jun 18, 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 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.

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.

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:
ML Engineer - Recommender Systems (Entry Level)

ML Engineer - Recommender Systems (Entry Level)

YouTube

San Francisco, CA โ€ข On-site

Full-time

This job post hasย expired today.ย Applications are no longer accepted.


Job description

A leading video platform is hiring an entry-level Machine Learning Engineer in San Francisco. In this full-time role, you will work on recommendation systems to improve user experience. Candidates should have a strong foundation in Computer Science and be proficient in machine learning.

Excellent problem-solving and communication skills are essential, as well as the ability to work collaboratively in a team-oriented environment. #J-18808-Ljbffr