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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.
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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.
PhD Fall Machine Learning Intern (ATG - Visual, Multimodal, and Recommender Systems)

PhD Fall Machine Learning Intern (ATG - Visual, Multimodal, and Recommender Systems)

Pinterest

Manhattan, NY • On-site, Remote

Other

Posted 8 days ago

Job description

About Pinterest:

Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we're on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.

Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other's unique experiences and embrace theflexibility to do your best work. Creating a career you love? It's Possible.

At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we're looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we'll explore your foundational skills and how you collaborate with AI.

Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here.

As a machine learning intern in our Advance Technology Group at Pinterest, you will be exposed to a full spectrum of ML product development. The team focuses on developing cutting-edge technologies for Pinterest's visual understanding modules and recommender systems. You'll conduct research that can be applied across Pinterest engineering teams and engage in external collaborations and mentoring, while also having opportunities to deploy features to hundreds of millions of users or conduct research applicable for paper submissions. We offer a 12-week fall internship program remotely or in our San Francisco, Palo Alto, Seattle, or New York offices.

Note to applicants:

By applying to this role, you will be considered for multiple intern roles open across our various ML teams. Please only apply once within the USA or Canada as multiple applications may delay our recruitment process.

Internships are 12 weeks paid from September 21 - December 11, 2026. Depending on the team, our fall internships will be located either remote or hybrid in San Francisco, Palo Alto, New York or Seattle offices.


What you'll do:

  • Develop and launch new user features using unique internal datasets and ML techniques, especially in recommendation systems, computer vision, representation learning, generative AI, and responsible AI.
  • Gain hands-on experience with production ML systems, including algorithmic research, infrastructure, data engineering, training, inference, and product, to deliver innovative solutions. You will be exposed to full-stack production ML systems.
  • Leverage frontier AI tools and agents to accelerate engineering implementation, including prototyping and experimentation work.
  • Validate AI-generated outputs through testing, code review, and critical thinking, ensuring solutions are accurate, maintainable, secure, and aligned with team standards.
  • Use AI to better understand unfamiliar code, investigate bugs, and summarize technical context or documentation.
  • Contribute in cutting-edge research in machine learning and artificial intelligence that can be applied to Pinterest problems
  • Write clean, efficient, and sustainable code
  • Take proactive ownership over the completion and quality of your tasks and project with minimal guidance from your mentor, manager, and peers


What we're looking for:

  • This role will be on our Visual Search or Applied Science teams. We are looking for candidates with experience in Computer Vision, Visual Search, User Understanding, Recommendation Systems, Reinforcement Learning, ML efficiency optimization, Generative AI, and LLMs.
  • Ability to legally work full time (40 hours/week) from September-December 2026
  • Working towards a PhD degree in Computer Science, ML, NLP, Statistics, Information Sciences or related field
  • Mastery of at least one systems language (Java, C++, Python) and one ML framework (Tensorflow, Pytorch, MLFlow)
  • Proficiency with AI-native engineering, including the design of agent-friendly codebases.
  • High degree of autonomy in learning new agent-first development tools.
  • Strong critical thinking when working with AI-generated suggestions, with a clear approach to validating correctness, performance, security, and maintainability.
  • Comfort iterating on prompts, refining workflows, and adapting AI-assisted approaches based on the problem, context, and constraints.
  • Experience in research and in solving analytical problems
  • Strong communicator and team player. Being able to find solutions for open-ended problems.

Preferred Qualifications:

    • Publications in machine learning, AI, data science, data analytics, statistics, or related technical fields
    • Strong passion for research and for answering hard questions with research
    • Passion for applied ML and the Pinterest product

Why Intern at Pinterest?

  • Meaningful Work: Contribute to projects that impact millions of users worldwide.
  • Mentorship: Learn from and be guided by experienced engineers and researchers in the field.
  • Growth and Development: Participate in professional development workshops and networking events to build your skills and connections.


In-Office Requirement Statement:

  • We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection
  • This role may require you to be located near an office for in-person collaboration, and therefore may need to be located a commutable distance from one of our Pinterest offices.

At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.

Information regarding the culture at Pinterest and benefits available for this position can be found here.


US based applicants only

The salary for this position is $12,100 monthly.

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Our Commitment to Inclusion:

Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, marital status, status as a protected veteran, physical or mental disability, medical condition, genetic information or characteristics (or those of a family member) or any other consideration made unlawful by applicable federal, state or local laws. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require a medical or religious accommodation during the job application process, please completethis formfor support.

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