Machine Learning Engineer - E-Commerce Recommendation Live

At TikTok, this Machine Learning Engineer role supports the end-to-end recommendation stack powering TikTok Shop LIVE. The work focuses on building and scaling recommendation systems that connect what people watch in live rooms with what they discover and buy, using foundation-model approaches across retrieval, ranking, reward modeling, and online optimization.

You will join the Global E-commerce Recommendation Live Algorithm team, which owns the full pipeline from retrieval and pre-ranking to ranking, mixed ranking, and long-term value modeling. The goal is to deliver consistent impact on GMV, buyer growth, and creator success across US and global markets, with a unified stack serving public discovery and private follower relationships.

Responsibilities

• Scale Large Recommendation Models (LRM): Push the scaling frontier of a unified ranking foundation model, including model capacity, sparse MoE, ultra-long user sequences, pre-training, and teacher-student distillation. Translate offline scaling gains into online improvements under strict latency and compute budgets using dynamic compute allocation and on-device real-time inference.
• Generative recommendation on multimodal Semantic IDs: Design semantic IDs that fuse visual, speech, text, collaborative, and product signals, including live context such as what is being showcased, pinned, or auctioned. Power generative retrieval with near-real-time SID serving.
• LLM4Rec and MLLM cross-domain modeling: Use multimodal LLMs to place live rooms, short videos, and products into one semantic space. Inject world knowledge to transfer user interest across content, search, and shopping, and address cold start for new creators, new products, and new markets.
• Reward models and post-training for recommenders: Build reward models for conversion, long-term value, and user experience. Align generative recommenders with SFT and DPO/GRPO-style RL post-training, generator-evaluator decoding, and listwise objectives.
• Agent x Recommendation: Build LLM agents that auto-tune multi-objective value weights and traffic mechanisms. Design and read experiments, diagnose online cases, and move from hand-tuned heuristics toward agentic optimization and multi-step RL.
• Long-term value and ecosystem: Model LTV, uplift, and multi-touch attribution. Design value and traffic mechanisms for live auctions, brands, and emerging creators while balancing short-term GMV with user experience and a healthy, growing creator ecosystem.
• Own it end to end: Work across problem framing, data pipelines, large-scale training, online serving, A/B testing, and launch, partnering closely with engineering, product, and data science.

Requirements

• Bachelor’s degree or above in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related field.
• 3+ years of industry experience in recommendation, search, advertising, or other large-scale applied machine learning, with a track record of shipped models that moved online metrics.
• Strong machine learning fundamentals and hands-on depth in at least one area such as retrieval and ranking, sequential user modeling, multi-task learning, generative recommendation, or LLM/multimodal modeling.
• Proficiency in Python and/or C++, plus solid experience with PyTorch or TensorFlow, including distributed training and model optimization.
• Ability to turn ambiguous business problems into modeling solutions and to own iteration from offline experiments to online A/B results.

Technologies

• Python
• C++
• PyTorch
• TensorFlow

About the Team

• Live commerce is described as a particularly challenging recommendation problem at TikTok.
• The team owns the end-to-end recommendation stack for TikTok Shop LIVE, spanning retrieval and pre-ranking through ranking, mixed ranking, and long-term value modeling.
• A unified stack supports seven core surfaces across public discovery and private follower relationships.
• The team is rebuilding the system around foundation models, including large recommendation models, generative retrieval on multimodal semantic IDs, multimodal LLMs for content-commerce bridging, reward models with RL post-training aligned to long-term value, and AI agents that tune and evolve the system.
• What the team ships is intended to directly drive GMV, buyer growth, and creator success for TikTok Shop in the US and globally.

Preferred Qualifications

• Experience in e-commerce or live-streaming recommendation, ads ranking, or other real-time, conversion-driven systems.
• Hands-on experience with large recommendation models or generative recommendation, including scaling laws, MoE, long-sequence modeling, pre-training, or distillation.
• Experience with LLM/MLLM post-training (SFT, reward modeling, RLHF, DPO, GRPO, or other RL-based alignment), ideally applied to recommendation or ranking.
• Experience building LLM agents (tool use, planning, automated experimentation, or parameter tuning) or applying reinforcement learning to multi-step decision-making such as traffic allocation.
• Experience with cross-domain transfer, LTV/long-term value modeling, uplift modeling and causal inference, or multi-objective optimization.
• Experience with large-scale training and inference efficiency (mixed precision, model parallelism, GPU serving optimization).
• Publications at top venues such as NeurIPS, ICML, ICLR, KDD, SIGIR, WWW, RecSys, ACL, or CVPR, or strong results in major ML competitions.

Compensation and Location

• Location: Seattle, WA (onsite)
• Salary: USD 207,480 - 368,220 per year
• Job code: A254886

Benefits

• Day one access to medical, dental, and vision insurance
• 401(k) savings plan with company match
• Paid parental leave
• Short-term and long-term disability coverage
• Life insurance
• Wellbeing benefits
• 10 paid holidays per year
• 10 paid sick days per year
• 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure)
• Additional discretionary bonuses/incentives (eligible may apply)
• Restricted stock units (eligible may apply)

Additional Information

• Employees may be eligible for additional discretionary bonuses/incentives and restricted stock units; benefits may vary by employment nature and country work location.
• For Los Angeles County (unincorporated) candidates: qualified applicants with arrest or conviction records will be considered in accordance with applicable laws, including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Criminal history may relate to certain job duties and could result in withdrawal of a conditional offer. The role may involve interacting with internal/external clients or colleagues, handling confidential information, and exercising sound judgment.

Accommodation and Company Background

• TikTok is committed to reasonable accommodations in recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs, or other reasons protected by applicable laws. Assistance can be requested at https://tinyurl.com/RA-request.
• TikTok is described as a leading destination for short-form mobile video, with global headquarters in Los Angeles and Singapore, and offices in multiple other cities.