Machine Learning Engineer Graduate (Brand Ads) - 2027 Start (PhD)

Description

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.

Requirements

Minimum Qualifications
- Individuals who are completing or have recently completed a PhD degree in Computer Science or a related discipline.
- Solid programming skills in one or more general-purpose programming languages, including but not limited to Go, C/C++, Java, or Python.
- Familiarity with basic data structures, algorithms, and Linux development environment.
- Strong coding, debugging, and system design fundamentals.
- Strong analytical thinking skills and essential knowledge of statistics, probability, and machine learning fundamentals.
- Good theoretical grounding in machine learning and deep learning concepts and techniques.
- Familiarity with at least one mainstream machine learning framework, such as TensorFlow, PyTorch, or MXNet.
- Comfort with ambiguity and the judgment to make clear engineering trade-offs.
- Strong communication and collaboration skills, with the ability to work effectively in a cross-functional environment.

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 distributed computing or large-scale machine learning systems, such as Spark, Flink, TensorFlow, or similar platforms.
- Relevant internship, project, or research experience in advertising, recommendation, forecasting, optimization, or large-scale machine learning systems.

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