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About This Role
Technology
Machine Learning Engineer - E-Commerce Recommendation Live
Location :
Seattle
Employment Type :
Regular
Job Code :
A254886
Responsibilities
About the Team Live commerce may be the hardest recommendation problem at TikTok. The "item" is a live room hosted by a real person: the product on display, the price, the crowd and even the auction clock change by the second. Purchase signals are sparse, intent shifts in real time, and a single session can take a user from a short video to a live room to checkout in under a minute. It is also a business growing at remarkable speed. Content-driven live commerce has already reshaped retail in another major market, turning a content platform into one of its top three e-commerce players within a few years. TikTok Shop is now scaling that model rapidly across the US and global markets, with live commerce at the heart of its growth. 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. One unified stack serves seven core surfaces, spanning public discovery and private follower relationships: For You feed live previews, creator-avatar LIVE entries, the swipe-up LIVE feed, LIVE discovery, Shop, Following and Inbox. We are rebuilding this stack around foundation models: large recommendation models that follow scaling laws, generative retrieval on multimodal semantic IDs, multimodal LLMs that bridge content and commerce, reward models and RL post-training that align recommenders with long-term value, and AI agents that tune, debug and evolve the system alongside us. What we ship directly drives GMV, buyer growth and creator success for TikTok Shop in the US and across global markets. We are looking for experienced engineers and researchers who want to define the next generation of recommendation for live commerce, and see their ideas go live at TikTok scale. Responsibilities: - Scale Large Recommendation Models (LRM): Push the scaling frontier of our unified ranking foundation model (model capacity, sparse MoE, ultra-long user sequences, pre-training and teacher-student distillation), and turn offline scaling gains into online wins under strict latency and compute budgets, with 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 right now, and 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 in one semantic space; inject world knowledge to transfer user interest across content, search and shopping, and crack 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, 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, and diagnose online cases, moving from hand-tuned heuristics to 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: From problem framing and data pipelines to large-scale training, online serving, A/B testing and launch, partnering closely with engineering, product and data science.
Qualifications
Minimum Qualifications - 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 models that shipped and moved online metrics. - Strong machine learning fundamentals and hands-on depth in at least one of: retrieval and ranking, sequential user modeling, multi-task learning, generative recommendation, or LLM / multimodal modeling. - Proficiency in Python and/or C++, and 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. 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 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.
Job Information
【For Pay Transparency】Compensation Description (Annually)
The base salary range for this position in the selected city is $207480 - $368220 annually.
Compensation may vary outside of this range depending on a number of factors, including a candidate’s qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role 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. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).
The Company reserves the right to modify or change these benefits programs at any time, with or without notice.
For Los Angeles County (unincorporated) Candidates:
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:
- Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
- Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
- Exercising sound judgment.
About TikTok
TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.
Why Join Us
Inspiring creativity is at the core of TikTok's mission. Our innovative product is built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and bring joy - a mission we work towards every day.
We strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. Every challenge is an opportunity to learn and innovate as one team. We're resilient and embrace challenges as they come. By constantly iterating and fostering an "Always Day 1" mindset, we achieve meaningful breakthroughs for ourselves, our company, and our users. When we create and grow together, the possibilities are limitless. Join us.
Diversity & Inclusion
TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At TikTok, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.
TikTok Accommodation
TikTok is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or a reasonable accommodation, please reach out to us at https://tinyurl.com/RA-request
Salary Context
This $207K-$368K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At TikTok, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($287K) sits 36% above the category median. Disclosed range: $207K to $368K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
TikTok AI Hiring
TikTok has 45 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Scientist, AI Agent Developer. Positions span San Jose, CA, US, Seattle, WA, US, Singapore, Singapore. Compensation range: $208K - $456K.
Location Context
AI roles in Seattle pay a median of $226,100 across 1,045 tracked positions. That's 6% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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