Machine Learning Scientist Intern (-Local Services) - 2027 Start (PhD) San Jose Technolog

Machine Learning Scientist Intern (Tik Tok-Local Services) - 2027 Start (PhD)

Location:

San Jose

Intern

A98722B

Tik Tok Location Based Service Team's goal is to deliver user-facing products based on the location that connects people with places, localized content, and service in the real world around the globe. We leverage POI data, geolocation, and large-scale location-based services (LBS) technologies to provide comprehensive LBS product and service capabilities for overseas businesses. We are looking for talented individuals to join us for an internship.

PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts.

• Work on ML models and algorithms to drive the creation of cutting-edge technical solutions to bridge the gap between technical capabilities and real-world business impact, including POI data production, POI recommendation, reviews sentiment analysis and ranking, geocoding, user profiling, etc.
• Responsible for algorithmic strategies in geographical content and services, enhancing and optimizing Location-Based Services (LBS) capabilities to support a range of international products.
• Thoroughly comprehend business intricacies, analyze issues, and devise appropriate algorithms and strategies tailored to various problem domains. Continuously optimize performance and efficacy.

• Currently pursuing a PhD in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
• Able to commit to working for 12 weeks during the Summer of 2027.
• Experienced in programming and algorithmic skills, proficient in languages such as Python, Golang, or C++.
• Deep understanding of computer architectures, data structures and algorithms.
• Experienced in one or more of the following topics:
  Tensorflow, Caffe, MxNet, PyTorch or other machine learning frameworks.
• Have good team communication and collaboration skills.

• Experience or domain expertise in one or more of the following areas is preferred: POI (Point of Interest) data algorithms, NLP, recommendation systems, reviews sentiment analysis and ranking, NER, text mining, geocoding, user profiling, etc.
• Experience in big data, data warehouse, streaming data flow and real time data processing system is preferred.

By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here:

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.

• day one access to health insurance, life insurance, wellbeing benefits and more.
• 10 paid holidays per year.
• paid sick time (56 hours if hired in first half of year, 40 if hired in second half of year).
• housing allowance if not working 100% remote.

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

• Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
• Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems;
• Exercising sound judgment.

Tik Tok is the leading destination for short-form mobile video. At Tik Tok, our mission is to inspire creativity and bring joy. Tik Tok'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.

Inspiring creativity is at the core of Tik Tok's mission. Our innovative product is built to help people authentically express themselves, discover and connect - and our global, diverse teams make that…