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How much do recommender systems jobs pay per year?

As of Jul 14, 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:
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Recommender Systems jobs near you
Infographic showing various Recommender Systems job openings in the United States as of July 2026, with employment types broken down into 1% Locum Tenens, 38% As Needed, 3% Full Time, 28% Temporary, and 30% Nights. Highlights an 90% Physical, 3% Hybrid, and 7% Remote job distribution, with an average salary of $111,995 per year, or $53.8 per hour.
Machine Learning Engineer (Recommender Systems)

Machine Learning Engineer (Recommender Systems)

Turn2Partners

Washington, DC • On-site

$129K - $155K/yr

Full-time

Posted 20 days ago

Job description

This Turn2 client is a fast-growing consumer tech company that is hiring a Machine Learning Engineer to build real-time recommendation and ranking systems for a widely used AI-driven shopping assistant. This is a high-impact, high-ownership role ideal for someone who thrives in fast-paced environments, ships quickly, and wants to shape how users experience search, personalization, and pricing across millions of products.
Why This Role Stands Out:
  • Immediate user impact: Your models power a real-world product used daily by a rapidly growing customer base.
  • Full ownership: Architect, build, and ship systems from scratch in a fast-moving, product-centric culture.
  • Startup velocity: Join a team of high-agency builders working to redefine how people shop.

What You'll Do:
  • Design large-scale systems to ingest and normalize data from 50+ external platforms, processing hundreds of millions of product listings.
  • Build and deploy end-to-end ML pipelines for ranking, recommendation, and personalization.
  • Collaborate with frontend and backend engineers to tightly integrate models into both web and app experiences.
  • Prototype backend services that support rapid experimentation and user-facing iteration.
  • Continuously optimize inference pipelines for latency, performance, and relevance.

What You Bring:
  • 2+ years of hands-on experience building and deploying machine learning models in production.
  • Proven ability to ship features in fast-moving, consumer-facing environments.
  • Expertise in personalization, ranking models, embeddings, and real-time inference (PyTorch preferred).
  • Experience building data pipelines for large-scale training and predictions.
  • Proficient in Python and familiar with backend tech such as GraphQL, Node.js, gRPC, or Prisma.
  • Solid understanding of cloud platforms (AWS, GCP, or Azure) and deployment best practices.
  • A tinkering mindset-someone who builds side projects and thrives in early-stage product environments.

Bonus Points For:
  • Experience with real-time recommendation or search ranking systems at scale.
  • Exposure to fullstack development or a willingness to contribute across the stack.
  • Familiarity with applied AI in consumer tech or e-commerce settings.

Apply