ZipRecruiter

Log In

1

Recommender Systems Jobs (NOW HIRING)

84.51˚

Lead Data Scientist (P3764)

Chicago, IL · On-site

They are seeking a Lead Data Scientist to provide technical leadership in search and recommender systems, focusing on modern model architectures and machine learning. The role involves developing ...

84.51°

Lead Data Scientist (P3764)

Cincinnati, OH · On-site

Own and drive technical initiatives across search & recommender systems. Define and evolve the science strategy for improving content discovery, relevance, personalization, and decision support ...

84.51˚

Lead Data Scientist (P3764)

Cincinnati, OH · On-site

They are seeking a Lead Data Scientist to provide technical leadership across search and recommender systems, focusing on modern model architectures and production-ready machine learning.

Sofascore

$115.30K - $158.30K/yr

Develop, evaluate, and optimize recommender systems, feed systems, and NLP-driven solutions (e.g., sentiment analysis, semantic search). * Implement robust MLOps practices using Docker, Kubernetes ...

xAI

Applied AI Engineer

Palo Alto, CA · On-site

... recommendation systems and machine learning algorithms. Responsibilities : • Designing and architecting recommendation algorithms across various product surfaces • Leverage all of xAI's infra and ...

Meta

Research Engineer, MRS AI

Bellevue, WA · On-site

$121.99K - $181K/yr

Meta is seeking a Research Engineer to join our Meta Recommendation Systems (MRS) AI Algorithm Team. Join us to build Meta's User Intelligence Engine - a unified platform that models who the user is ...

Faire

Data Science Intern

San Francisco, CA

$75/hr

Our Data Science team builds and maintains the algorithmic systems - spanning search, personalization, recommendation, and ranking - that power our marketplace and help our customers thrive. We are ...

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 30, 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.
Sr. Machine Learning Engineer, Monetization Engineering

Sr. Machine Learning Engineer, Monetization Engineering

Pinterest

Remote

$107K - $146.90K/yr

Full-time

Posted 16 days ago

Job description

Job Summary:
Pinterest is a platform that inspires creativity and provides innovative opportunities for its users. They are seeking a Sr. Machine Learning Engineer to develop and execute a vision for the machine learning technology stack within Ads, focusing on personalization and enhancing user experience through advanced ML techniques.
Responsibilities:
• Build cutting edge technology using the latest advances in deep learning and machine learning to personalize Pinterest
• Partner closely with teams across Pinterest to experiment and improve ML models for various product surfaces (Homefeed, Ads, Growth, Shopping, and Search), while gaining knowledge of how ML works in different areas
• Use data driven methods and leverage the unique properties of our data to improve candidates retrieval
• Work in a high-impact environment with quick experimentation and product launches
• Keep up with industry trends in recommendation systems
• Leverage LLMs to enhance content understanding
Qualifications:
Required:
• 2+ years of industry experience applying machine learning methods (e.g., user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, and graph representation learning)
• Degree in computer science, statistics, or related field; or equivalent experience
• End-to-end hands-on experience with building data processing pipelines, large scale machine learning systems, and big data technologies (e.g., Hadoop/Spark)
• Practical knowledge of large scale recommender systems, or modern ads ranking, retrieval, targeting, marketplace systems
Preferred:
• M.S. or PhD in Machine Learning or related areas
• Publications at top ML conferences
• Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring
• Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration
• Expertise in scalable realtime systems that process stream data
• Passion for applied ML and the Pinterest product
• Background in computational advertising
Company:
Pinterest is a visual bookmarking tool for saving and discovering creative ideas. Founded in 2010, the company is headquartered in San Francisco, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

Apply