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How much do recommender systems jobs pay per year?

As of Jul 29, 2026, the average yearly pay for recommender systems in California is $110,529.00, according to ZipRecruiter salary data. Most workers in this role earn between $70,600.00 and $134,200.00 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.

What are the most commonly searched types of Recommender Systems jobs in California? The most popular types of Recommender Systems jobs in California are:
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What job categories do people searching Recommender Systems jobs in California look for? The top searched job categories for Recommender Systems jobs in California are:
Recommender Systems jobs near you
Infographic showing various Recommender Systems job openings in California as of July 2026, with employment types broken down into 1% Locum Tenens, 31% As Needed, 19% Full Time, 3% Part Time, 22% Temporary, and 24% Nights. Highlights an 89% Physical, 3% Hybrid, and 8% Remote job distribution, with an average salary of $110,529 per year, or $53.1 per hour.

Director, ML Research Science (Adtech / Recommender Systems)

Cognitiv

San Mateo, CA • Hybrid

$250K - $330K/yr

Other

Re-posted 18 days ago

Job description

The role

We are seeking a technical leader who can balance strategic leadership with hands-on contributions. You'll oversee a growing team of ML research scientists, guide innovation in deep learning and LLMs, and directly advance Cognitiv's real-time bidding and recommendation systems. This role is critical to our success, sitting at the intersection of cutting-edge research and production-scale delivery.

Location: This position will be located in San Mateo, CA with a hybrid work schedule of 3 days in office (Mon/Tue/Wed) and 2 days remote (Thursday/Friday).

What You'll Do
  • Lead and Mentor. You manage and grow a team of Machine Learning Research Scientists, fostering a collaborative, innovative environment while mentoring individuals on both technical challenges and career development.
  • Set Strategic Direction. You define and execute the vision for machine learning research within the adtech domain, representing the team in strategic discussions and contributing to company-wide initiatives.
  • Drive Technical Innovation. You oversee the design and implementation of cutting-edge deep learning architectures, staying current with LLM research and guiding the integration of new breakthroughs into Cognitiv's solutions.
  • Stay Hands-On. You actively contribute through coding, experimentation, and code reviews, ensuring technical excellence and adherence to best practices.
  • Advance AdTech Performance. You continuously improve models and algorithms to drive ad targeting, real-time bidding performance, and audience relevance.
  • Enable Scalable Systems. You collaborate with operations, engineering, and cross-functional partners to refine data pipelines, model deployment, and monitoring systems.
  • Deliver Results. You manage project timelines, resources, and deliverables, ensuring successful completion of high-impact research initiatives.
Tech Stack
  • Core Tools - Python, PyTorch, deep learning architectures (transformers, recommendation models).
  • Traditional ML - XGBoost, PCA.
  • Big Data / Infra - Spark, Hadoop, distributed training systems.
  • Cloud Platforms - AWS, GCP, or Azure.
  • Bonus - C++.
Who You Are
  • Experienced Leader with Advanced Education: Master's or Ph.D. in Computer Science, Statistics, Electrical Engineering, or a related field, with 5-7+ years of experience in machine learning R&D. Proven experience leading teams of researchers and senior ICs/PhDs while remaining 30-50% hands-on (coding, reviews, experimentation).
  • Deep Learning, LLMs & Model Tuning: Deep technical expertise in PyTorch, transformers, and Large Language Models (LLMs), including large-scale training and fine-tuning of deep neural networks.
  • Machine Learning Breadth: Strong understanding of both deep learning and traditional ML techniques (e.g., XGBoost, PCA), with the ability to apply the right approach to the right problem.
  • Engineering Excellence: Proficiency in Python with strong foundations in algorithms, data structures, and software engineering principles; experience building models in real-time, high-throughput systems (e.g., recommender systems, adtech).
  • Production Experience: Hands-on experience developing, deploying, and optimizing machine learning models in production environments, including distributed systems, cloud platforms (AWS, GCP, Azure), and big data frameworks (Hadoop, Spark).
  • Strong Communicator: Excellent written and verbal communication skills, strong project management capabilities, and the ability to drive alignment in fast-paced, dynamic environments.
Bonus Points If You Have
  • AdTech & RTB Experience. Prior exposure to advertising technology and real-time bidding (RTB) systems is a strong plus.
  • Distributed Systems & Cloud. Familiarity with big data frameworks (Spark, Hadoop) and cloud platforms (AWS, GCP, Azure).
  • C++ Skills. Strong C++ programming ability is a significant advantage alongside Python expertise.
  • Research & Community Impact. A track record of published research or meaningful contributions to the machine learning community.
  • Bridging Research and Delivery. Experience managing both exploratory research timelines and production-grade delivery cycles.

Salary: $250,000 - $330,000 USD Base Salary + Equity

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