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About the Role
The Commerce & Growth Intelligence team seeks a Staff Machine Learning Engineer to develop systems integrating ML capabilities into customer-facing products. This role involves model inference, distributed ML workloads, data processing, and integration with cloud services and on-device intelligence on Apple devices. You will influence system architecture and build software connecting components, from inference services to fine-tuning workflows for large datasets.
Responsibilities
- Lead engineering projects across the AI/ML stack, collaborating with modeling, infrastructure, and product teams.
- Turn emerging capabilities into production software.
- Lead engineering systems and integrations supporting training, deployment, and product use.
- Collaborate on model iteration and fine-tuning.
- Combine depth in a core area with the ability to work across system boundaries.
- Choose and extend open-source infrastructure.
- Design interfaces between components.
- Resolve practical constraints involving performance, reliability, compute resources, and deployment environments.
Requirements
- Experience shipping production ML systems or ML-powered applications with ownership of significant architecture and implementation decisions.
- Deep expertise in at least one relevant area, such as model inference, distributed computing, ML infrastructure, or data systems for ML.
- Strong programming and software engineering skills, including system design, API design, testing, and debugging complex software.
- Practical experience integrating ML models into software systems and understanding their execution and deployment requirements.
- A track record of leading technical initiatives across teams, resolving architectural tradeoffs, and delivering working systems.
- Experience investigating performance and reliability problems and driving improvements based on measured results.
- Ability to communicate technical decisions clearly and build alignment across disciplines.
- Bachelor’s degree in Computer Science, Computer Engineering, Information Systems, or Electronic Engineering and 12+ years of relevant work experience.
Skills
- Model inference
- Distributed computing
- ML infrastructure
- Data systems for ML
- System design
- API design
- Testing
- Debugging complex software
- Distributed ML execution
- Model serving
- Ray
- Ray Serve
- Model optimization
- ONNX Runtime
- Core ML
- Distributed training
- Fine-tuning
- Job orchestration
- Checkpointing
- GPU workloads
- Accelerator workloads
- Large-scale data processing
- Spark
- Flink
- Go
- Swift
- Python for ML workflows
- Python for data processing
- Python for tooling
- Python for backend services
- Agent workflows
- Tool integration
- LangGraph
- Model Context Protocol (MCP)
Experience Level
- 12+ years of relevant work experience
- 8+ years of relevant work experience
Education Level
- Bachelor’s degree in Computer Science, Computer Engineering, Information Systems, or Electronic Engineering
- Master’s degree in Computer Science, Computer Engineering, Information Systems, or Electronic Engineering
About the Company
- Our work spans model inference, distributed ML workloads, data processing, and integration with cloud services and on device intelligence on Apple devices.