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Manager Recommender Systems information

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$46K

$112K

$197K

How much do manager recommender systems jobs pay per year?

As of Jul 26, 2026, the average yearly pay for manager 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 the difference between Manager Recommender Systems vs Data Scientist?

AspectManager Recommender SystemsData Scientist
CredentialsAdvanced degree in CS, ML, or related field; experience with recommender algorithmsDegree in CS, Statistics, or related; strong programming and analytical skills
Work EnvironmentLeading teams, overseeing recommender system projects, collaborating with product teamsAnalyzing data, building models, interpreting results across various domains
Industry UsageTech companies, e-commerce, streaming services

While both roles require strong technical skills and data expertise, Manager Recommender Systems focus on leading teams and managing recommender system projects, whereas Data Scientists primarily analyze data and develop models across diverse applications.

What are the key skills and qualifications needed to thrive as a Manager, Recommender Systems, and why are they important?

To thrive as a Manager, Recommender Systems, you need a solid background in computer science, machine learning, and data analytics, typically supported by a relevant degree and experience in building recommendation algorithms. Familiarity with programming languages like Python or Scala, machine learning frameworks, and big data platforms such as Spark or Hadoop is essential, along with knowledge of A/B testing and model evaluation techniques. Strong leadership, project management, and cross-functional communication skills distinguish top performers in this role. These skills ensure effective team guidance, robust system development, and alignment of technical solutions with business goals in a fast-evolving digital landscape.

What are Manager Recommender Systems?

A Manager of Recommender Systems is a professional who oversees the development and deployment of algorithms that suggest products, services, or content to users based on their preferences and behavior. They lead teams of data scientists, engineers, and analysts to design, implement, and optimize recommendation engines. Their role involves strategic planning, project management, and ensuring that the recommender systems align with business goals while delivering a personalized user experience.

How does a Manager of Recommender Systems typically collaborate with data scientists and engineers to deliver effective recommendation solutions?

As a Manager of Recommender Systems, you will frequently coordinate cross-functional efforts between data scientists, machine learning engineers, and product teams. Your role involves setting project priorities, facilitating communication to ensure clear understanding of objectives, and removing roadblocks that may impede progress. You’ll also oversee the translation of business requirements into technical solutions, review algorithm performance, and guide the team in iterative model improvements. Regular collaboration ensures that the recommendations delivered align with both user needs and business goals.
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What cities are hiring for Manager Recommender Systems jobs? Cities with the most Manager Recommender Systems job openings:
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Manager Recommender Systems jobs near you
Infographic showing various Manager Recommender Systems job openings in the United States as of July 2026, with employment types broken down into 86% Full Time, 13% Part Time, and 1% Contract. Highlights an 94% Physical, 2% Hybrid, and 4% Remote job distribution, with an average salary of $111,995 per year, or $53.8 per hour.
Director, ML Research Science (Adtech / Recommender Systems)

Director, ML Research Science (Adtech / Recommender Systems)

Cognitiv

San Mateo, CA • Hybrid

$250K - $330K/yr

Other

Posted 15 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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