TikTok
Machine Learning Engineer Intern (E-Commerce Recommendation Mall)- 2027 Summer
Seattle, Washington, United States of America · Intern · Internship
Sponsorship not specifiedDetected 28 min ago
PythonAlgorithmsMachine LearningDeep LearningTensorFlowPyTorchLLMsResearchProblem Solving
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odds of building a lasting career here
42Risky
Cap-exempt (no lottery)0
Sponsors this role100
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
Thin sponsorship signal and lottery-bound. A low-probability bet with your clock running. Prioritize cap-exempt roles and proven entry-level sponsors first.
Lottery odds assume a STEM candidate.
Personalize to your clock →Employer immigration record
from this employer's Department of Labor filings
Green-card filing pattern in this occupation
Context, not a finding about this posting: of this employer's 106 green-card filings in this occupation, 98% were for a worker who already held the job.
Green-card follow-through: 92%
Of 155 labor certifications old enough to have been used, 12 expired without the employer filing the next step. Median time from filing to decision: 496 days.97% of their filings were for a worker who already held the job.Only certifications past the 180-day window are counted — recent ones cannot have expired yet.
Files H-1B transfers
69 transfer filings in the last year, covering 69 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.
Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.
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About the role
- 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.
Responsibilities
- Generative Recommendation Research: Drive the evolution of recommender systems from discriminative to generative paradigms; explore frontier directions such as generative retrieval and generative re-ranking/blending; continuously improve personalization capabilities while deeply optimizing the training and inference efficiency of generative models on GPUs.
- Agentic Recommendation Research: Explore the construction of self-evolving agents and leverage agents to continuously optimize recommender systems; drive the evolution of recommender systems toward agentic architectures capable of keenly perceiving user context and making real-time, personalized decisions and adjustments.
- Long-Term Value and User Experience Modeling: Explore replacing traditional heuristic rule-based systems with LLM and agent capabilities; build next-generation algorithms for measuring and optimizing long-term value (LTV) and user experience, enabling sustainable growth of the platform ecosystem.
- Drive the evolution of recommender systems from discriminative to generative paradigms; explore frontier directions such as generative retrieval and generative re-ranking/blending; continuously improve personalization capabilities while deeply optimizing the training and inference efficiency of generative models on GPUs.
- Explore the construction of self-evolving agents and leverage agents to continuously optimize recommender systems; drive the evolution of recommender systems toward agentic architectures capable of keenly perceiving user context and making real-time, personalized decisions and adjustments.
- Explore replacing traditional heuristic rule-based systems with LLM and agent capabilities; build next-generation algorithms for measuring and optimizing long-term value (LTV) and user experience, enabling sustainable growth of the platform ecosystem.
Nice to have
- Experience in recommendation systems, especially in live commerce, e-commerce, search, ads, or other large-scale consumer products.
- Experience with generative recommendation, large recommendation models, retrieval and ranking systems, or related recommendation architecture upgrades.
- Publications in top-tier conferences such as NeurIPS, ICML, ICLR, KDD, ACL, CVPR, SIGIR, or RecSys, or strong achievements in major technical competitions.
- Preferred Requirement:
Company info
- Introduction:
- Our E-commerce Recommendation Team is responsible for building up and scaling our recommendation system to provide the best shopping experience for our TikTok users.
- 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.
- Please clearly state your availability in your resume, including your start and end dates.
This listing is sourced directly from TikTok's careers page and normalized into a canonical job model.