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Recommender Systems Jobs (NOW HIRING)

ClifyX

Senior Data scientist

Cincinnati, OH · On-site

The role involves developing recommender systems, applying generative AI models, and collaborating across teams to deliver data-driven solutions. Responsibilities : • Recommender Systems ...

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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:
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 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.

ML Engineer - Personalization & Recommendation Systems

krea.ai

San Francisco, CA • On-site

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

Join to apply for the ML Engineer - Personalization & Recommendation Systems role at krea.ai.

About Krea

At Krea, we are building next-generation AI creative tools. We are dedicated to making AI intuitive and controllable for creatives. Our mission is to build tools that empower human creativity, not replace it. We believe AI is a new medium that allows us to express ourselves through various formats—text, images, video, sound, and even 3D. We're building better, smarter, and more controllable tools to harness this medium.

This Job

We’re looking for an ML Engineer to architect and build Krea’s personalization and recommendation systems from scratch. You’ll have full ownership over how we understand user taste, curate content, and adapt generative models to individual aesthetics. This is a role at the intersection of recommendation systems, representation learning, and generative image and video tools.

What You’ll Do
  • Architect and build Krea’s personalization and recommendation stack from the ground up, owning the technical direction end to end.
  • Design algorithms to model user preference and taste, enabling Krea’s models to adapt to individual styles and aesthetics.
  • Build high-quality, curated feeds that balance exploration, personalization, and aesthetic coherence.
  • Work directly with the model and research team to co-design personalization mechanisms that influence how our generative models learn, adapt, and express style.
  • Contribute to personalized image generation research, with a focus on style, taste and subjective quality.
  • Collaborate closely with product, design, and research to define what “good personalization” means in a creative context.
  • Take systems from research and prototyping through production, iteration, and continuous improvement.
What We’re Looking For
  • Strong experience building recommendation systems or personalized feeds from scratch.
  • Proven ability to design and ship high-quality curated content experiences.
  • Experience working with media-based personalization (image, video preferred; music or other modalities also welcome).
  • Solid foundations in machine learning, representation learning, and modern deep learning techniques.
  • Strong Python skills and experience with ML frameworks such as PyTorch or JAX.
  • Ability to operate independently, make architectural decisions, and own complex systems end to end.
Bonus points
  • Experience with large-scale data systems and production ML infrastructure.
  • Prior work on or familiarity with diffusion models or generative image systems.
  • Background in embeddings, similarity search, ranking, or aesthetic evaluation.
  • Interest in creative tools, art, design, or generative media.
What we offer
  • Openness to sponsoring international candidates (e.g., STEM OPT, OPT, H-1B, O-1, E-3).
  • Work alongside a world-class team building the future of AI creative tooling.
  • Significant scope and company-wide impact.
  • Competitive compensation (75th percentile of market) with meaningful equity.
Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Engineering and Information Technology

Industries

Artists and Writers

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