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Manager Recommender Systems Jobs (NOW HIRING)

Analyzes, troubleshoots, and resolves system hardware, software, and networking issues; and provides status reports to management. * Recommends systems technology solutions and enterprise-related ...

Analyzes, troubleshoots, and resolves system hardware, software, and networking issues; and provides status reports to management. * Recommends systems technology solutions and enterprise-related ...

$115K - $158K/yr

This comes easy with Torneo, our very own tournament management software for lower leagues. The ... Develop, evaluate, and optimize recommender systems, feed systems, and NLP-driven solutions (e.g ...

Job Summary The Systems Manager is responsible for overseeing and managing the company's enterprise ... Stays abreast of emerging tech trends and best practices, recommend system enhancements, and drive ...

Job Summary The Systems Manager is responsible for overseeing and managing the company's enterprise ... Stays abreast of emerging tech trends and best practices, recommend system enhancements, and drive ...

Job Summary The Systems Manager is responsible for overseeing and managing the company's enterprise ... Stays abreast of emerging tech trends and best practices, recommend system enhancements, and drive ...

In this role, you'll develop deep technical expertise in our recommender system, but you'll spend ... To be clear: this is a data scientist role, not a product manager role. You'll still be hands-on ...

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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 5, 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.
More about Manager Recommender Systems jobs
What cities are hiring for Manager Recommender Systems jobs? Cities with the most Manager Recommender Systems job openings:
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 Manager Recommender Systems jobs? States with the most job openings for Manager Recommender Systems jobs include:
Infographic showing various Manager Recommender Systems job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 91% Physical, 2% Hybrid, and 7% Remote job distribution, with an average salary of $111,995 per year, or $53.8 per hour.
Research Scientist Intern (TikTok Recommendation-LLMs, RL, GenAI) - 2026 Start (PhD)

Research Scientist Intern (TikTok Recommendation-LLMs, RL, GenAI) - 2026 Start (PhD)

TikTok

San Jose, CA • On-site

$60/hr

Internship

Medical, Life

Posted 9 days ago


TikTok rating

7.6

Company rating: 7.6 out of 10

Based on 11 frontline employees who took The Breakroom Quiz

124th of 202 rated software companies


Job description

Responsibilities
Are you passionate about pushing the boundaries of recommendation systems? Do you dream of working on cutting-edge technologies that shape the way hundreds of millions of people discover content? If so, we invite you to join TikTok's US Core Recommendation Team as a PhD student and embark on an exciting journey of innovation. Our team's mission is to elevate TikTok's personalized content discovery and user experiences to unprecedented heights. By constantly stretching the limits of deep learning and large-scale system design, we're determined to make remarkable strides in recommendation precision, user involvement, and scalability, all to cater to the needs of hundreds of millions of users in the US. As a PhD student in our team, you will be at the forefront of developing the next generation of recommendation systems. Your work will be pivotal in enhancing the user experience by delivering more accurate, personalized, and engaging content recommendations. You will have the opportunity to delve into several groundbreaking directions, including but not limited to: - End-to-End Generative Large Recommendation Systems: We are committed to reimagining the traditional recommendation pipelines. You will explore novel architectures, algorithms, and optimization strategies to break through the limitations of existing systems. By challenging the status quo, you will strive to build more efficient, scalable, and generative recommendation frameworks. - Ultra-Long Sequence Modeling of User Lifecycle Behavior: Understanding user behavior over an extended period is crucial for providing long-term personalized recommendations. You will focus on modeling the ultra-long sequences of user interactions throughout their lifecycle on TikTok. - Integrating LLM and Multimodal Technologies for Recommendation: With the abundance of multimodal content (text, image, video, audio) on TikTok, integrating LLM and multimodal technologies into recommendation systems is essential. You will work on leveraging the power of LLMs to understand and process information, and combine it with other multimodal data to enable seamless multimodal-recommendation fusion. - Posttraining & RL: Exploration of posttraining methods to better align large generative models with business and feed quality needs. Conduct original research on applying RL (e.g., bandit models, policy optimization, offline RL) to recommendation problems (such as diversity & multiobjective fusion problems) We are looking for talented individuals to join our team in 2026. As a PhD Intern, you will get unparalleled opportunities for you to kickstart your career, pursue bold ideas and explore limitless growth opportunities. Co-create a future driven by your inspiration with TikTok. We will prioritize candidates who are able to commit to work with the team for 12 weeks. Please state your availability for the internship in your resume. Applications will be reviewed on a rolling basis. We encourage you to apply early. Responsibilities - Conduct in-depth research and development in the aforementioned groundbreaking directions, designing and implementing innovative algorithms to enhance recommendation performance and accuracy. - Analyze large-scale user behavior data and content data to gain insights and drive model improvements. - Participate in the deployment and evaluation of the developed recommendation systems in real-world scenarios, ensuring their practical effectiveness. - Collaborate with cross-disciplinary teams, including infrastructure engineers, PMO, and researchers, to create advanced systems that improve recommendation relevance, diversity, and user engagement.
Qualifications
Minimum Qualifications - Currently pursuing a PhD degree in Computer Science, Electrical Engineering, Statistics, or a related field, with a focus on recommendation systems, natural language processing, or multimodal learning. - Strong theoretical foundation and hands-on research experience in relevant areas. - Proficiency in Python and familiarity with ML frameworks such as PyTorch or TensorFlow. - Solid foundation in data structures, algorithms, and analytical and problem solving skills. Preferred Qualifications - First-author publications in top-tier conferences such as NeurIPS, ICML, ACL, CVPR, or KDD. - Experience with large-scale machine learning systems or applied research in industry. - Prior work or research integrating LLMs or multimodal models into real-world applications. - Familiarity with reinforcement learning, bandit algorithms, or offline RL for recommender systems.
Job Information
[For Pay Transparency]Compensation Description (Hourly) - Campus Intern
The hourly rate range for this position in the selected city is $60- $60.
Benefits may vary depending on the nature of employment and the country work location. Interns have day one access to health insurance, life insurance, wellbeing benefits and more. Interns also receive 10 paid holidays per year and paid sick time (56 hours if hired in first half of year, 40 if hired in second half of year). Interns who are not working 100% remote may also be eligible for housing allowance.
The Company reserves the right to modify or change these benefits programs at any time, with or without notice.
For Los Angeles County (unincorporated) Candidates:
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:
1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
3. Exercising sound judgment.
About TikTok
TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.
Why Join Us
Inspiring creativity is at the core of TikTok's mission. Our innovative product is built to help people authentically express themselves, discover and connect - and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and bring joy - a mission we work towards every day.
We strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. Every challenge is an opportunity to learn and innovate as one team. We're resilient and embrace challenges as they come. By constantly iterating and fostering an "Always Day 1" mindset, we achieve meaningful breakthroughs for ourselves, our company, and our users. When we create and grow together, the possibilities are limitless. Join us.
Diversity & Inclusion
TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At TikTok, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.
TikTok Accommodation
TikTok is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or a reasonable accommodation, please reach out to us at

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