Machine Learning Engineer Graduate (Commerce Ads) - 2027 Start

Join TikTok as a Graduate Machine Learning Engineer supporting Commerce Ads, starting in 2027 at the San Jose office on an onsite basis. In this role, you will help build scalable machine learning systems and models that strengthen ads ranking quality while optimizing how advertisers’ marketing strategies perform.

The work blends strong software fundamentals with applied machine learning, including experimentation to improve model accuracy and collaboration across product and design teams to shape next-generation shopping experiences on TikTok.

What you’ll do

• Develop highly scalable machine learning systems and state-of-the-art models to improve ads ranking quality and optimize advertisers’ marketing strategies, including applications such as click-through rate prediction, conversion rate prediction, intelligent format selection, and user journey optimization.
• Explore, develop, and run experiments on new features aimed at improving model accuracy.
• Work with ads platform objectives and use modern machine learning to enhance ads relevance, quality, and the quantity delivered to end users.
• Collaborate with Product Managers, Designers, and other teams to help define and build next-generation shopping experiences on TikTok.

Qualifications

• Completing or recently completed a Bachelor’s degree or above in Computer Science, Computer Engineering, or a related technical discipline.
• Strong programming ability, including Go, C/C++, and Python; familiarity with data structures and algorithms; and comfort working in a Linux development environment.
• Analytical problem-solving skills, with essential knowledge and skills in statistics.
• Solid theoretical grounding in machine learning and deep learning concepts, including architectures such as CNN, RNN, and LSTM.
• Familiarity with the architecture and implementation of at least one mainstream ML framework, including TensorFlow, PyTorch, or MXNet.

Technology focus

• Go, C/C++, Python, Linux
• CNN, RNN, LSTM
• TensorFlow, PyTorch, MXNet
• Spark

Compensation & benefits

• Base salary range: USD 128,000 - 256,000 per year
• 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)

Preferred qualifications

• Knowledge in at least one of: ads bidding & auction, ads quality control, or online advertising systems, including familiarity with terms such as CPC/CPM, CTR/CVR, Ranking/Targeting, Conversion/Budget, Campaign/Creative, and Demand/Inventory, as well as DSP/RTB.
• Experience with resource management and task scheduling for large-scale distributed software, such as Spark and TensorFlow.
• Relevant work or research experience in search and recommendation.

Additional application details

• Job code: A39632
• Location: San Jose
• Employment type: Regular
• Successful candidates must be able to commit to an onboarding date by the end of the year. Please state availability and graduation date clearly in your resume.
• Candidates can apply to a maximum of two positions and will be considered in the order you apply.
• Applications will be reviewed on a rolling basis, and candidates are encouraged to apply early.
• 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. The role may involve interacting with clients/colleagues (including occasional unsupervised contact), handling confidential information, and managing access to IT systems, which may relate to conditional employment decisions.
• TikTok accommodations: TikTok provides reasonable accommodations in recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs, or other reasons protected by applicable laws. Requests can be made at https://tinyurl.com/RA-request.