Machine Learning Engineer - Search Ads
Job Description
Onsite machine learning engineer role in San Jose, CA on TikTok's Search Ads team, delivering large-scale ads systems with NLP, ranking, and optimization across the TikTok app ecosystem.
Responsibilities
- Contribute to the development of a large-scale Ads system.
- Own relevance modeling and strategy optimization, including semantic matching, active learning, multimodal (text, photo, video) modeling, and ranking approaches.
- Develop and iteratively improve Ads algorithms using machine learning techniques.
- Advance NLP capabilities and query understanding, covering query classification, seq2seq models, named entity recognition, knowledge graphs, and bidword optimization.
- Improve CTR and CVR estimation accuracy through data analysis, modeling, and feature engineering.
- Research and implement Ads pacing and traffic-control algorithms.
- Collaborate with product managers and strategy/operations teams to define product direction and feature sets.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, or a related field.
- Strong programming, debugging, and optimization skills in general-purpose programming languages.
- Ability to think critically and articulate solutions clearly and concisely.
Technologies
- Go
- C/C++
- Python
Benefits
- Base salary range: 156,000 - 316,800 USD per year
- Potential discretionary bonuses or incentives
- Restricted stock units
- 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
For Los Angeles County (Unincorporated) Candidates
- Interacting with internal or external clients and colleagues, including unsupervised contact as needed
- Appropriately handling and safeguarding confidential information, including proprietary and trade secrets, and access to information systems
- Exercising sound judgment in relevant scenarios
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