Apple is seeking a Senior ML Infrastructure Engineer to design, build, and scale the foundational systems that power their machine learning lifecycle within the Siri User Experience Metrics team, aiming to improve Siri's User Experience across all Apple platforms.
Requirements
- Proficient in Python with solid knowledge of software design principles.
- Expertise in designing and implementing distributed systems or data pipelines (e.g., Spark, Flink, Kafka, Airflow) and knowledge of SQL to analyze data and derive insights.
- Experience with ML lifecycle tools (e.g., MLflow, Metaflow, Kubeflow, SageMaker, Vertex AI).
- Hands-on experience with container orchestration and cloud-native services (e.g., Kubernetes, Docker, AWS/GCP/Azure).
- Experience with real-time model serving and streaming pipelines (e.g., Kafka, Flink, Ray Serve, Triton).
- Experience with any ML authoring framework (PyTorch, TensorFlow, JAX, etc.), particularly on-device ML frameworks such as CoreML, TFLite or ExecuTorch.
Responsibilities
- Designing and maintaining high-throughput, low-latency pipelines for real-time and batch inference.
- Automating the model training and evaluation workflows with reproducibility and traceability in mind.
- Defining infrastructure standards and best practices for ML experimentation, CI/CD, and observability.
- Collaborating with ML researchers and engineers to improve productivity through tooling and platform enhancements.
- Lead the design, development, and scaling of our machine learning infrastructure.
- Building robust, scalable, and maintainable infrastructure to support the full ML lifecycle - from data ingestion and feature computation to training, deployment, and monitoring in production.
Other
- Leadership experience, including being a technical lead for complex, cross functional development projects demonstrating good technical judgement and prioritization skills.
- Strong communication skills and a proactive, ownership-driven mindset.
- Prior experience architecting ML platforms or Feature Stores in a fast-paced production environment.
- Experience optimizing GPU and CPU resource allocation for training and inference workloads.
- Laser-focused on impact - bringing sharp programming skills, strong problem-solving abilities and clear communication to the table, all driven by a passion for building exceptional products.
