Machine Learning Engineer - E-Commerce Recommendation Live

Join TikTok in Seattle to build the end-to-end recommendation stack powering TikTok Shop LIVE.

Responsibilities

• Scale Large Recommendation Models (LRM): Push scaling for a unified ranking foundation model, including model capacity, sparse MoE, ultra-long user sequences, and pre-training with teacher-student distillation.
• Convert offline scaling gains into online wins 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 what is being showcased, pinned, or auctioned in a live room).
• Power generative retrieval with near-real-time SID serving.
• LLM4Rec and MLLM cross-domain modeling: Use multimodal LLMs to represent live rooms, short videos, and products in one semantic space.
• Inject world knowledge to transfer user interests 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 recommenders with SFT and DPO/GRPO-style RL post-training.
• Apply generator-evaluator decoding and listwise objectives for training and optimization.
• Agent x Recommendation: Build LLM agents to auto-tune multi-objective value weights and traffic mechanisms, design and read experiments, and diagnose online cases.
• 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.
• Balance short-term GMV with user experience and a healthy, growing creator ecosystem.
• Own it end to end: Drive work from problem framing and data pipelines through large-scale training, online serving, A/B testing, and launch; partner with engineering, product, and data science.

Requirements

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

Technologies

• Python
• C++
• PyTorch
• TensorFlow

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)

About the Team

• Live commerce is described as the hardest recommendation problem at TikTok.
• The Global E-commerce Recommendation Live Algorithm team owns the end-to-end recommendation stack for TikTok Shop LIVE, from retrieval and pre-ranking to ranking, mixed ranking, and long-term value modeling.
• A unified stack serves seven core surfaces, covering public discovery and private follower relationships.
• The team is rebuilding the stack around foundation models, including:
  • Large recommendation models following scaling laws
  • Generative retrieval on multimodal semantic IDs
  • Multimodal LLMs connecting content and commerce
  • Reward models and RL post-training aligned to long-term value
  • AI agents that tune, debug, and evolve the system
• What the team ships directly drives GMV, buyer growth, and creator success for TikTok Shop in the US and globally.

Location

• Seattle, WA (onsite)

Compensation

• USD 207,480 - 368,220 per year
• Base salary range in the selected city: $207480 - $368220 annually
• Employees may be eligible for additional discretionary bonuses/incentives and restricted stock units
• Benefits may vary depending on the nature of employment and the country work location

Job Details

• Job code: A254886
• For Pay Transparency

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 in LLM/MLLM post-training (SFT, reward modeling, RLHF, DPO, GRPO, or other RL-based alignment), ideally for recommendation or ranking
• Experience building LLM agents (tool use, planning, automated experimentation, or parameter tuning) or applying reinforcement learning to multi-step decision-making like 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

Accommodation

• TikTok provides reasonable accommodations in recruitment for candidates with disabilities, pregnancy, sincerely held religious beliefs, or other protected reasons.
• Request assistance here: https://tinyurl.com/RA-request

About TikTok

• TikTok is described as the leading destination for short-form mobile video.
• Global headquarters: Los Angeles and Singapore.
• Additional offices: New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.

Los Angeles County (Unincorporated) Fair Chance

• Qualified applicants with arrest or conviction records will be considered in accordance with federal, state, and local laws, including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.
• Criminal history may have a direct, adverse, and negative relationship on certain job duties and could result in withdrawal of a conditional offer.
• Role may involve interacting and occasionally having unsupervised contact with internal/external clients or colleagues.
• Role requires appropriately handling confidential information and access to IT systems.
• Requires exercising sound judgment.