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TikTok · New York, NY · Mid Level · Data, analytics and AI · posted 2 days ago
TikTok · San Jose, CA · posted 53 days ago
By AtiveSite · Published 07/11/2026 · Updated 09/11/2026 · Every figure is linked to its source below.
| Employer | TikTok |
|---|---|
| Location | San Jose, California |
| Field | Technology › Data, analytics and AI |
| Level | Mid Level |
| Source category | Business Operations |
| Posted | 2026-09-17 (53 days ago) |
| Work arrangement | On-site at the location listed |
| Pay | Not published in the source listing |
Responsibilities
Team Introduction
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.
We believe recommendation is being rewritten in the compute era. ID-based collaborative filtering and supervised learning built today's systems and still run most of the industry - but their returns are diminishing, and we are betting the next order of magnitude on rebuilding the stack on LLM foundations. Our ambition is to build the most advanced recommendation system in the world, and the next generation after that.
We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth.
Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.
Responsibilities:
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 pre-training; 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 modelling, repurchase and retention, transaction attribution, fatigue modeling, new-user recommendation, incremental value modelling, interest exploration, LLM4Rec - problems where industry has no standard answers. We expect, and support, original research: internal papers, patents, and publication at top external venues.
Qualifications
Minimum Qualifications:
- 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.
Preferred Qualifications:
- 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.
Job Information
[For Pay Transparency] Compensation Description (annually)
The base salary range for this position in the selected city is $162000 - $387600 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:
1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
3. Exercising sound judgment.
Field: Technology Sub-niche: Data, analytics and AI All jobs at TikTok Jobs in California Jobs in San Jose, CA San Jose city guide California state guide Santa Clara County economy
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This is a Mid Level role in the Technology field (Data, analytics and AI) at TikTok. Mid-level roles usually expect some years of relevant experience and the ability to work with limited supervision.
We could not match this job title to one occupation in the official wage statistics, so no pay estimate is shown. Pay is not in the source listing; ask the employer for the range and compare in the 240 job niches.
Estimate for a wage earner: 2026 federal brackets and standard deduction (IRS), Social Security 6.2% up to $184,500, Medicare 1.45% (+0.9% above $200,000 single / $250,000 married), and a state rate you set; set your own state tax rate. It ignores credits, pre-tax deductions, local taxes and benefits. Sources: IRS: tax year 2026 inflation adjustments · SSA wage base per Federal Register notice 2025-19763.
This is editorial guidance, not information from the employer.
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The job is in San Jose, CA. Below is what the official statistics say about living and working there.
How San Jose compares. Among the 145 largest places in California, San Jose is #3 by population, #30 for household income (#1 = highest), #110 for rent (#1 = cheapest), #48 for college degrees (#1 = most). Its county, Santa Clara County, is #1 of 58 counties for average pay ($197,043).
San Jose has 990,138 residents (ACS 2020–2024), about 2.5% of the population of California. It covers 178 square miles of land, which works out to roughly 5,564 people per square mile. The median age is 38.2, about the same as the state median (38.2 against 37.9).
The median gross rent is $2,669 a month (rent plus utilities), 31% above the state median ($2,669 against $2,036). Over a year that is 21.9% of the median household income. US housing guidance treats housing as affordable when it takes 30% of income or less, so the typical household is under that line. The median home value is $1,233,200, which is 8.4 times the median household income ($146,427); a ratio above 5 usually means buying is out of reach without savings or a second income. 55.8% of households own their home, about the same as the state average (55.8% against 55.9%).
The San Jose-Sunnyvale-Santa Clara, CA metropolitan area has 1,134,970 jobs in the BLS wage survey, and the median pay across all occupations is $84,050 a year, 65% above the national median ($84,050 against $50,980). The biggest occupation groups are computer and mathematical (149,970 jobs, median $186,320); management (115,110 jobs, median $210,750); business and financial operations (94,210 jobs, median $125,240). The area is most specialised in computer and mathematical (3.9 times) and architecture and engineering (2.5 times), where the share of workers is well above the national share. The best-paying groups with at least 5,000 jobs are management ($210,750) and legal ($195,190). In 2026M09 the unemployment rate of Santa Clara County was 4.2%. 67.6% of residents aged 16 and over are in the labour force. The city lies in Santa Clara County; its jobs, pay by industry and GDP are on the county page.
Among workers, 62.9% drive alone, 2.4% use public transit, 1.8% walk, 20.3% work from home. 15.3% of workers commute 45 minutes or more, 21% below the state average (15.3% against 19.4%).
47.3% of adults aged 25 and over hold a bachelor's degree or higher, 28% above the state share (47.3% against 37.1%). 42.0% of residents were born outside the United States, 56% above the state share (42.0% against 27.0%). The poverty rate is 8.1%, 32% below the state rate (8.1% against 12.0%).
The warmest month is Aug, averaging 68°F (19.8°C), and the coolest is Jan at 47°F (8.3°C). About 510 mm (20 inches) of precipitation falls in a year; Dec is the wettest month (114 mm) and Jul the driest (0 mm). These values come from NASA satellite-based climatology on a grid of about 50 km, so they describe the wider area, not a weather station in the city.
Closest cities in this guide: Campbell (8 miles, similar rent); Los Gatos (9 miles, rent 22% higher); Santa Clara (10 miles, rent 13% higher); Milpitas (10 miles, rent 17% higher). Rent differences are against San Jose; use the Compare tab to put any two cities side by side.
Full guide to San Jose →California guideAll jobs in San Jose
These links go to official sources and open in a new window. The first one is the government's or company's own site as listed in Wikidata (property "official website"); check it before relying on it.
Places within 50 miles (straight line) of San Jose, CA that have a guide, with what living there costs. No pay estimate is shown because the title did not match one occupation.
| Place ⇅ | Distance from the job city ⇅ | Median rent ⇅ | Median home value ⇅ | Median household income ⇅ | Commute 45+ min ⇅ | Rent as % of occupation pay ⇅ | Open jobs there ⇅ |
|---|---|---|---|---|---|---|---|
| San Jose, CA | 0 mi | $2,669 | $1,233,200 | $146,427 | 15.3% | n/a | Jobs |
| Campbell, CA | 8 mi | $2,737 | $1,617,000 | $145,268 | 10.4% | n/a | Jobs |
| Los Gatos, CA | 9 mi | $3,247 | $2,000,001 | $217,554 | 17.6% | n/a | — |
| Santa Clara, CA | 10 mi | $3,016 | $1,582,600 | $178,958 | 8.9% | n/a | Jobs |
| Milpitas, CA | 10 mi | $3,120 | $1,251,700 | $178,798 | 15.2% | n/a | Jobs |
| Saratoga, CA | 12 mi | $3,501 | $2,000,001 | $250,001 | 8.3% | n/a | — |
| Cupertino, CA | 13 mi | $3,501 | $2,000,001 | $234,707 | 9.1% | n/a | Jobs |
| Sunnyvale, CA | 13 mi | $3,039 | $1,801,800 | $186,170 | 9.0% | n/a | Jobs |
| Morgan Hill, CA | 15 mi | $2,474 | $1,210,200 | $163,920 | 32.9% | n/a | — |
| Fremont, CA | 15 mi | $2,933 | $1,403,800 | $181,506 | 23.6% | n/a | Jobs |
| Mountain View, CA | 16 mi | $3,062 | $1,927,000 | $189,917 | 9.3% | n/a | Jobs |
| Los Altos, CA | 16 mi | $3,501 | $2,000,001 | $250,001 | 8.3% | n/a | — |
| Los Altos Hills, CA | 18 mi | $3,501 | $2,000,001 | $250,001 | 18.0% | n/a | — |
| Newark, CA | 19 mi | $2,776 | $1,142,800 | $169,064 | 22.2% | n/a | — |
| Palo Alto, CA | 19 mi | $3,484 | $2,000,001 | $231,101 | 8.2% | n/a | Jobs |
| East Palo Alto, CA | 21 mi | $2,244 | $1,102,600 | $113,493 | 9.7% | n/a | — |
| Menlo Park, CA | 22 mi | $3,239 | $2,000,001 | $210,025 | 11.5% | n/a | Jobs |
| Gilroy, CA | 24 mi | $2,301 | $1,027,400 | $133,107 | 32.4% | n/a | — |
| Atherton, CA | 24 mi | $3,501 | $2,000,001 | $250,001 | 7.0% | n/a | — |
| Union City, CA | 24 mi | $2,713 | $1,104,900 | $133,715 | 24.0% | n/a | — |
We could not match this title to one occupation, so here are the other sub-niches in Technology, where your skills may carry over:
Cybersecurity (512) Software engineering (2678) Cloud, IT and infrastructure (743) Product management (570)
How far San Jose, CA is from the 20 largest US cities, in straight-line miles (not driving distance). Click a column to sort.
| From ⇅ | Straight-line distance (miles) ⇅ | Population of that city ⇅ |
|---|---|---|
| San Jose, CA | 0 mi | 990,138 |
| San Francisco, CA | 73 mi | 830,235 |
| Los Angeles, CA | 296 mi | 3,857,263 |
| San Diego, CA | 407 mi | 1,389,526 |
| Phoenix, AZ | 604 mi | 1,642,323 |
| Seattle, WA | 714 mi | 754,195 |
| Denver, CO | 930 mi | 718,877 |
| Oklahoma City, OK | 1,354 mi | 697,125 |
| Fort Worth, TX | 1,414 mi | 963,194 |
| San Antonio, TX | 1,444 mi | 1,479,835 |
| Dallas, TX | 1,446 mi | 1,307,930 |
| Austin, TX | 1,459 mi | 979,539 |
| Houston, TX | 1,602 mi | 2,328,253 |
| Chicago, IL | 1,832 mi | 2,711,226 |
| Indianapolis, IN | 1,923 mi | 885,860 |
| Columbus, OH | 2,087 mi | 914,802 |
| Charlotte, NC | 2,270 mi | 903,844 |
| Jacksonville, FL | 2,337 mi | 977,670 |
| Philadelphia, PA | 2,497 mi | 1,579,706 |
| New York, NY | 2,550 mi | 8,483,844 |
| City ⇅ | Straight-line distance ⇅ | Open jobs ⇅ |
|---|---|---|
| Campbell, CA | 8 mi | 7 |
| Milpitas, CA | 10 mi | 2 |
| Sunnyvale, CA | 13 mi | 19 |
| Fremont, CA | 15 mi | 2 |
| Palo Alto, CA | 19 mi | 217 |
| Capitola, CA | 23 mi | 1 |
| Pleasanton, CA | 26 mi | 6 |
| Redwood City, CA | 27 mi | 30 |
| Belmont, CA | 30 mi | 2 |
| Alameda, CA | 39 mi | 38 |
| San Francisco, CA | 73 mi | 918 |
| Sacramento, CA | 90 mi | 49 |
TikTok is the employer in this listing. See all its open jobs here →
Company description (from The Muse): As the leading destination for short-form mobile video, TikTok’s mission is to inspire creativity and bring joy.
size: Large Size; industries: Entertainment & Gaming, Social Media, Technology; locations on The Muse: Dubai, United Arab Emirates, London, United Kingdom, Los Angeles, CA, New York, NY, Paris, France, Singapore.
We do not hold headcount or revenue for this employer. Check its careers site and, if it is listed on a stock exchange, its public filings (SEC EDGAR search).
The employer's profile on The Muse →
What it is like to work at a company (culture, schedules, benefits) is not in the source data, so we do not describe it. Read the description and ask the questions under "How to prepare".
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A mid level role in technology (data, analytics and ai) listed 53 days ago. The full description is on this page and on The Muse.
San Jose, CA.
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The listing does not publish pay, and we could not match the title to one official occupation.
Median rent in San Jose is $2,669 a month and median household income $146,427 (ACS 2020–2024).
It was posted on 2026-09-17 (53 days ago). Jobs older than 90 days are not shown.
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