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About the Role
The Apple Ads Machine Learning Platform team's mission is to empower Ads teams to build and scale innovative ML systems that deliver highly optimized advertising content to consumers. You will work closely with ML engineers and scientists to design, develop, and build world-class platform capabilities that will enable Ads teams to improve and scale our ML features, models, and applications.
Responsibilities
- Design and develop secure and scalable back-end systems.
- Build high-performing, elegant systems from the ground up, in close partnerships with various teams.
- Select technologies and build the right solutions for unique ad network challenges.
- Build machine learning products which deliver on Apple's privacy commitments and change the way advertising works with data.
- Contribute to a culture that emphasizes reliability, simplicity, and scalability.
- Join a team of world-class machine learning engineers applying leading-edge technologies to deliver extraordinary experiences to customers.
Requirements
- Experience writing mission-critical code for production machine learning systems.
- Experience building ML infrastructure, frameworks or services used by multiple teams.
- Experience building SDKs, API, and automation UIs used by development teams.
- Proven experience building automation and CI/CD.
- Passionate about improving the developer experience for AI/ML practitioners.
- Solid understanding of the ML lifecycle: training, evaluation, deployment and serving/inferencing, with working experience building and deploying models.
- Solid understanding of model evaluation, train-serve skew and data drift.
- Working knowledge of deep learning architectures and training frameworks such as PyTorch or TensorFlow.
- Prior experience applying ML at scale in advertising, recommender systems, information retrieval or related domains.
- Strong problem solving, debugging and performance tuning skills.
- Results oriented, with the ability to communicate effectively, both written and verbal, with technical and non-technical multi-functional teams.
- Product-minded with a proven ability to seek projects with a sense of ownership.
- Experience in advertising industry.
- Experience with distributed training and/or optimizing large-scale models for low-latency serving.
- Experience building infrastructure for diverse sets of model development use cases including LLMs, multimodal models, classical ML, and reinforcement learning.
- Experience building and/or operationalizing foundation models.
- Experience with agentic AI.
- Prior experience in privacy-preserving ML.
Skills
- Machine Learning
- ML Infrastructure
- ML Frameworks
- ML Services
- SDKs
- API
- Automation
- CI/CD
- Developer Experience
- ML Lifecycle
- Model Training
- Model Evaluation
- Model Deployment
- Model Serving
- Model Inferencing
- Train-Serve Skew
- Data Drift
- Deep Learning
- PyTorch
- TensorFlow
- Advertising ML
- Recommender Systems
- Information Retrieval
- Problem Solving
- Debugging
- Performance Tuning
- Communication
- Ownership
- Distributed Training
- Large-Scale Model Optimization
- Low-Latency Serving
- LLMs
- Multimodal Models
- Classical ML
- Reinforcement Learning
- Foundation Models
- Agentic AI
- Privacy-Preserving ML
Experience Level
- 5+ years of machine learning experience
- 3+ years of engineering experience (PhD)
- 6+ years of engineering experience (MS)
- 7+ years of engineering experience (BS)
Education Level
- PhD in Computer Science or related field
- MS in Computer Science or related field
- BS in Computer Science or related field
About the Company
- At Apple, we work every day to create products that enrich people's lives.
- The Apple Ads group makes it possible for people around the world to easily access informative and imaginative content on their devices while helping publishers and developers promote and monetize their work.
- Today, our technology and services power advertising in the App Store, Apple Maps, and Apple News.
- Our platforms are highly-performant, deployed at scale, and setting new standards for enabling effective advertising while protecting user privacy.