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Internship Recommender Systems Jobs (NOW HIRING)

Sr. Software Engineer, FireTV Personalization

Seattle, WA · On-site

$139K - $183K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

... recommendation systems that help customers discover the right content at the right time across ... BASIC QUALIFICATIONS - 5+ years of non-internship professional software development experience - 5+ ...

SkillBridge Internship - Field Technician

Houston, TX · On-site

$18.75 - $25.75/hr

You will gain hands-on experience during your internship and will have the opportunity to join ... recommend system enhancements providing a value-added partner relationship Desired Military ...

SkillBridge Internship - Field Technician

Plano, TX · On-site

$19 - $25.75/hr

You will gain hands-on experience during your internship and will have the opportunity to join ... recommend system enhancements providing a value-added partner relationship Desired Military ...

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Internship Recommender Systems information

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How much do internship recommender systems jobs pay per hour?

As of Aug 15, 2026, the average hourly pay for internship recommender systems in the United States is $22.50, according to ZipRecruiter salary data. Most workers in this role earn between $17.31 and $24.52 per hour, depending on experience, location, and employer.

What is the difference between Internship Recommender Systems vs Data Analyst?

AspectInternship Recommender SystemsData Analyst
Required CredentialsTypically a background in computer science, data science, or related fields; familiarity with machine learning and recommender algorithmsDegree in statistics, mathematics, or related fields; proficiency in data analysis tools and programming languages
Work EnvironmentTech companies, startups, or online platforms focusing on personalized recommendationsBusiness, finance, healthcare, or marketing sectors analyzing data to inform decisions
Employer & Industry UsageUsed by companies developing recommendation engines for internships or job matching platformsEmployed across various industries to interpret data, generate reports, and support strategic decisions

Internship Recommender Systems focus on developing algorithms to match candidates with internships, requiring technical skills in machine learning. Data Analysts interpret and analyze data to support business decisions, often using statistical tools. While both roles involve working with data, their applications and skill sets differ significantly.

What is an internship recommender systems?

Internship recommender systems are digital tools or algorithms designed to help students and job seekers find internship opportunities that best match their skills, interests, and qualifications. By analyzing user profiles, preferences, and sometimes even past experiences, these systems suggest internships that are likely to be a good fit. They often use machine learning or artificial intelligence techniques to personalize recommendations and improve the matching process over time. Such systems are widely used by universities, career platforms, and large organizations to streamline the internship search and application process.

What types of projects can I expect to work on during an internship focused on recommender systems?

As an intern working on recommender systems, you’ll typically contribute to projects such as improving recommendation algorithms, analyzing user interaction data, and testing new personalization features. You might also collaborate with data scientists and engineers to prototype and evaluate models, conduct A/B tests to measure recommendation performance, and help refine data pipelines. The work often involves both independent research and teamwork, giving you exposure to a blend of technical implementation and real-world problem-solving in a collaborative environment.

What are the key skills and qualifications needed to thrive as an internship recommender systems engineer, and why are they important?

To excel as an Internship Recommender Systems Engineer, a strong background in computer science, statistics, and machine learning, often supported by relevant coursework or experience, is essential. Familiarity with programming languages like Python, data analysis libraries, machine learning frameworks (such as TensorFlow or PyTorch), and recommendation system algorithms is typically required. Analytical thinking, problem-solving skills, and the ability to collaborate effectively with teams help candidates stand out. These skills are crucial for designing, implementing, and optimizing recommendation systems that deliver accurate and personalized suggestions to users.
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What cities are hiring for Internship Recommender Systems jobs?

Cities with the most Internship 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 Internship Recommender Systems jobs?

States with the most job openings for Internship Recommender Systems jobs include:

Infographic showing various Internship Recommender Systems job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 9% Part Time, 4% Contract, and 1% Nights. Highlights an 89% Physical, 3% Hybrid, and 8% Remote job distribution, with an average salary of $46,809 per year, or $22.5 per hour.

Software Engineer Intern (Data Arch - E-commerce) - 2027 Summer

TikTok

Seattle, WA • On-site

$42.75/hr

Full-time, Temporary, Internship

Medical, Life

Posted 11 days ago


TikTok rating

8.2

Company rating: 8.2 out of 10

Based on 12 frontline employees who took The Breakroom Quiz

106th of 244 rated software companies


Job description

Responsibilities
Global E-commerce is a new and fast growing business that aims at connecting all customers to excellent sellers and quality products on TikTok Shop, through E-commerce live-streaming, E-commerce short videos, and commodity recommendation. Our E-commerce Recommendation Infra team is responsible for building up and optimizing the infrastructure for such recommendation systems, to provide the best experience for our users. We work closely with applied machine learning engineers and build scalable systems to support all kinds of innovative algorithms and large scale models. We are looking for talented individuals to join us for an internship. Our internship program offers students hands-on experience, industry exposure, and opportunities to apply their knowledge to real-world challenges while building a strong foundation for personal and professional growth. Interns will gain practical experience, explore potential career paths, and participate in social events, learning programs, and development workshops alongside industry professionals. Candidates may apply to a maximum of two positions across Our Company and its affiliates globally. Applications will be considered in the order they are submitted. Applications are reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume, including your start and end dates. Online Assessment Candidates who pass resume screening will be invited to participate in Our Company's technical online assessment. Responsibilities: This internship provides students the opportunity to join one of our engineering teams where you will have the opportunity to: - Build and maintain high performance online services for TikTok recommendation system. - Build extremely efficient and reliable data pipelines for candidates generation, profile generation, training examples generation, realtime online training, etc. - Work with your mentor to build globalized large-scale recommendation system. - Design and develop high performance computing frameworks and storage systems.
Qualifications
Minimum Qualifications: - Must be able to commit to a 12-week full-time work period during Summer 2027 - Currently pursuing an Undergraduate/Masters degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline. - Experience in programming, included but not limited to, the following programming languages: C, C++, Java or Golang. - Effective communication skills and a sense of ownership and drive. Preferred Qualifications: - Demonstrated software engineering experience from previous internship, work experience, coding competitions, or publications. - Demonstrated experience in one area of the following areas: personalized recommendations, search engine, machine learning, distributed storage system, big data frameworks is a plus. By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here: https://careers.tiktok.com/legal/privacy
Job Information
[For Pay Transparency]Compensation Description (Hourly) - Campus Intern
The hourly rate range for this position in the selected city is $42.75- $42.75.
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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