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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer Graduate (E-Commerce Content Recommendation - **Company:** Tiktok Shop - **Location:** Seattle, WA, United States - **Experience:** Starter - **Salary:** $153,900.0 - $300,960.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Nvidia CUDA, Distributed Computing Environment, Intrusion Detection Systems, Linux Kernel, Open Source Technology, Recommender Systems, Large Language Models, Deep Learning, Kaggle, Information Technology - **Published:** September 18, 2026 - **Apply:** https://www.themuse.com/jobs/tiktok/machine-learning-engineer-graduate-ecommerce-content-recommendation-generative-large-recommendation-model-2027-start-phd ## About the Role Global E-Commerce (TikTok Shop) is one of TikTok's fastest-growing businesses and a core driver of the company's revenue growth. Our Global E-Commerce Content Recommendation team owns the end-to-end recommendation stack for e-commerce video and image-text content on TikTok worldwide - retrieval, ranking, and multi-queue blending; supply ecosystem and cold start; and the browsing-to-purchase experience for hundreds of millions of users., Individuals who are completing or have recently completed a PhD degree in Computer Science, AI, Mathematics, Statistics or a related discipline - Solid ML and engineering fundamentals: you understand the math behind the models, and you write clean, efficient, reproducible code with a strong command of algorithms and data structures. - Deep research or engineering practice in at least one of: LLMs / foundation models, NLP, CV, RL, or recommendation / search / ads - and you can articulate why you made the choices you made, and where they fell short. - Genuine enthusiasm for LLM / LRM techniques: you want frontier methods live in production, not parked at offline metrics. - Strong problem definition and decomposition: faced with an ambiguous problem that has no standard answer, you find your own foothold., Publications at KDD, SIGIR, RecSys, WWW, ACL, NeurIPS, ICML, ICLR, or comparable venues - or high-quality open-source work. - CUDA / Triton kernel development, source-level deep-learning-framework optimization, large-scale distributed training, or high-performance inference deployment. - Hands-on experience with LLM post-training (SFT / RLHF / DPO / GRPO), agent-system construction, or inference acceleration. - Led or deeply contributed to a key project in search, ads, recommendation, or large models, with a complete problem-to-online-impact loop. - Awards in ACM-ICPC, NOI, Kaggle, or comparable competitions. - Heavy user of AI coding and agentic workflows for building systems and optimizing models. ## Description Team Introduction, You will help build - and rewrite - an industrial recommendation system serving a billion-scale user base across short-video, livestream, and product scenarios, covering retrieval, pre-ranking, ranking, and blending end to end. Every iteration ships to production and directly moves user experience and GMV. - Scale recommendation models like LLMs. Push ranking models from hundreds of millions to billions of parameters and chart the scaling laws of recommendation: behavior-corpus pretraining; multi-scenario, multi-task, multi-stage joint training; ultra-long behavior-sequence modeling (10K+ events) with KV caching, sequence compression, user/generation (U-G) disaggregated serving, speculative decoding, and dynamic batching - raising MFU while holding a strict millisecond latency budget. - Build one-stage generative retrieval. Reframe retrieval as generation: tokenize the item space into semantic IDs (RQ-VAE / SID) and train autoregressive models, grounded in MLLM semantics, to generate what a user wants next - collapsing the traditional "multi-channel retrieval + ranking" funnel into a single generative stage. The open problems span the full stack: item tokenizers that balance semantic content against collaborative signal, and SIDs that stay stable while millions of new items arrive daily; post-training the generator directly on live user feedback (preference optimization, GRPO-style RL); and decoding under a millisecond budget - beam search, decoding constrained to the valid item space, and test-time scaling that trades inference compute for better recommendations. The prize is a system freed from its path dependence on ID memorization, where cold-start generalization comes from semantics rather than impression history. - Inject world knowledge. Use large models' real-world knowledge to mine latent user interests and semantic representations beyond what pure ID co-occurrence can express; use reasoning models to run explicit chain-of-thought inference over long-horizon user intent, making the system materially better at discovery and novelty. - Push training and inference to the hardware limit. Custom CUDA / Triton fused kernels, memory and computation-graph optimization, distributed training and inference acceleration, mixed precision and low-bit quantization - engineered for what makes recommendation hard: sparse embeddings, variable-length sequences, and many task heads. - Rewrite R&D with agents. We are embedding coding agents deep into the algorithm-development loop: automated feature mining and pipeline generation, experiment configuration and training orchestration, automated evaluation and online-diagnosis attribution, bad-case mining and patrol. You will be both a user and a builder of this system. - Do original work on open problems. Long-term value modeling, repurchase and retention, transaction attribution, fatigue modeling, new-user recommendation, incremental value modeling, interest exploration, LLM4Rec - problems where industry has no standard answers. 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