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How much do recommender systems jobs pay per year?

As of Jun 19, 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.

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Recommender Systems jobs near you

Member of Technical Staff - Recommendation Systems

xAI

Palo Alto, CA • On-site

Full-time

Posted 4 days ago

Job description

Job Summary:
xAI is a company dedicated to creating AI systems that enhance human understanding of the universe. They are seeking exceptional Applied engineers to work on a high-priority project focused on recommendation systems and ranking algorithms, impacting approximately 600 million monthly users.
Responsibilities:
• Designing and architecting recommendation algorithms across various product surfaces
• Leverage all of xAI’s infra and AI stacks to dramatically enhance the user experience
• Write data pipelines and training jobs that continuously learn from product data.
• Iterate and improve the algorithm by gathering user feedback in real time through experimentation
• Ensuring scalability and efficiency of machine learning systems
Qualifications:
Required:
• Knowledge of data infrastructure like Kafka, Clickhouse, and Spark
• Experienced in implementing recommender systems and/or deep learning applications at industrial scale
• Skilled in one or more DL software frameworks such as JAX or PyTorch
Preferred:
• Exceptional candidates may be experienced in writing CUDA kernels
Company:
XAI is an artificial intelligence startup that develops AI solutions and tools to enhance reasoning and search capabilities. It is a sub-organization of SpaceX. Founded in 2023, the company is headquartered in Palo Alto, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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