San Jose, California, United StatesFull TimeEntry-levelPosted Today
The Brand Ads Team builds technologies that unlock business growth potential. This team owns several ads products: reservation ads, auction ads, and innovative content ads that enables advertisers and users to foster more awareness of their brand to attain their business goals. We work on the end-to-end ads delivery tech stack, including ads bidding, ranking, and forecasting.
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.
Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.
Responsibilities:
- As a Machine Learning Engineer on the Brand Ads team, you will work on real-world problems such as forecasting available inventory, optimizing auction traffic, and using machine learning to improve delivery quality and efficiency. You will collaborate closely with engineers, product managers, data scientists, and strategy partners to turn ambiguous business challenges into scalable technical solutions.
-You will work on a brand-facing business where your systems map directly to revenue and to the experience of major advertisers.
Minimum Qualifications
- Individuals who are completing or have recently completed a Master's degree in Computer Science, a related field, or equivalent practical experience.
- Proficient in at least one general-purpose programming language, such as Go, C/C++, Java, or Python, with solid coding and debugging skills.
Strong foundation in data structures, algorithms, Linux development environment, and system design, with strong analytical and problem-solving skills.
- Solid understanding of statistics, probability, machine learning, and deep learning fundamentals, with familiarity with at least one mainstream machine learning framework such as TensorFlow, PyTorch, or MXNet.
- Ability to work effectively in ambiguous environments, make sound engineering trade-offs, and collaborate in cross-functional teams.
Preferred Qualifications
- Good understanding of online advertising systems, especially one or more of the following areas: guaranteed delivery, reservation ads, pacing, allocation, inventory forecasting, traffic strategy, brand safety, ads ranking, bidding, or auction systems.
- Familiarity with advertising concepts such as CPM, CPC, CTR, CVR, campaign, creative, targeting, demand, inventory, budget, pacing, reservation, auction, DSP, or RTB.
- Experience with large-scale backend systems, recommendation systems, search systems, or advertising systems.
- Experience with data analysis, experimentation, feature engineering, or model optimization.
- Experience with large-scale backend, recommendation, search, or advertising systems, as well as data analysis, experimentation, feature engineering, model optimization, distributed computing, or related internship, project, or research experience.
By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here: https://careers.tiktok.com/legal/privacy
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.
Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.
Responsibilities:
- As a Machine Learning Engineer on the Brand Ads team, you will work on real-world problems such as forecasting available inventory, optimizing auction traffic, and using machine learning to improve delivery quality and efficiency. You will collaborate closely with engineers, product managers, data scientists, and strategy partners to turn ambiguous business challenges into scalable technical solutions.
-You will work on a brand-facing business where your systems map directly to revenue and to the experience of major advertisers.
Minimum Qualifications
- Individuals who are completing or have recently completed a Master's degree in Computer Science, a related field, or equivalent practical experience.
- Proficient in at least one general-purpose programming language, such as Go, C/C++, Java, or Python, with solid coding and debugging skills.
Strong foundation in data structures, algorithms, Linux development environment, and system design, with strong analytical and problem-solving skills.
- Solid understanding of statistics, probability, machine learning, and deep learning fundamentals, with familiarity with at least one mainstream machine learning framework such as TensorFlow, PyTorch, or MXNet.
- Ability to work effectively in ambiguous environments, make sound engineering trade-offs, and collaborate in cross-functional teams.
Preferred Qualifications
- Good understanding of online advertising systems, especially one or more of the following areas: guaranteed delivery, reservation ads, pacing, allocation, inventory forecasting, traffic strategy, brand safety, ads ranking, bidding, or auction systems.
- Familiarity with advertising concepts such as CPM, CPC, CTR, CVR, campaign, creative, targeting, demand, inventory, budget, pacing, reservation, auction, DSP, or RTB.
- Experience with large-scale backend systems, recommendation systems, search systems, or advertising systems.
- Experience with data analysis, experimentation, feature engineering, or model optimization.
- Experience with large-scale backend, recommendation, search, or advertising systems, as well as data analysis, experimentation, feature engineering, model optimization, distributed computing, or related internship, project, or research experience.
By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here: https://careers.tiktok.com/legal/privacy