> Markdown version of [/jobs/ext/4583686-machine-learning-engineer-search-recommendation](https://www.wearedevelopers.com/jobs/ext/4583686-machine-learning-engineer-search-recommendation). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Machine Learning Engineer, Search Recommendation

- **Company:** Tiktok Inc
- **Location:** San Jose, CA, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Data Structures, Design of User Interfaces, Human-Computer Interaction, Search Algorithms, Machine Learning, Natural Language Processing, Recommender Systems, Web Traffics, Information Technology, Software Coding
- **Published:** November 5, 2026
- **Apply:** https://www.careerbuilder.com/job-details/machine-learning-engineer-search-recommendation-san-jose-ca--2c054e6b-cace-41c8-8343-9e12b5c9f795

## About the Role

Machine Learning, Machine Learning, eCommerce, Natural Language Processing (NLP), Sales, Search Algorithms, Website Traffic Growth, User Interface/Experience (UI/UX), Computer Science, Algorithms, * Explore the upper limits of short text recommendation and general recommendation technology, with a focus on the interaction between recommendation and NLP technology. Minimum Qualifications

 * 3+ years of work experience in machine learning. Good product sense with a focus on user experience.
 * Bachelor or advanced degree in computer science or a related technical discipline.
 * Excellent coding skills. Solid knowledge of data structure and algorithms.
 * Excellent in analysis, modeling and problem-solving, and can see the essence of problems from complex data.

## Description

* Enhance search recommendation services and models for TikTok, driving increased search traffic on TikTok and TikTok Mall, while also improving user s search understanding.
 * Optimize the recommender system based on hyperscale machine learning models, covering a range of tasks from recall/first-stage ranking to final-stage ranking in the end-to-end workflow.