Sr. AI Inference Platform Engineer - Jobs - Careers at Apple
- Design and build automations to evaluate AI inference performance across hardware generations and configurations.
- Develop tooling to surface performance trends, regressions, and insights to infrastructure and planning teams.
- Build projection and forecasting models to support long-term capacity planning decisions.
- Analyze performance and utilization data to identify bottlenecks, trends, and optimization opportunities.
- Partner with AI infrastructure engineers, hardware teams, and capacity planners to deliver critical data and tooling.
- Create and enhance performance analysis workflows to increase team velocity and data reliability.
- Continuously improve the accuracy, coverage, and usability of performance measurement and analysis systems.
- BS or MS in Computer Science or related technical field.
- Solid understanding of AI/ML inference architecture and the performance characteristics of serving systems.
- 7 or more years of experience with performance and infrastructure engineering in distributed systems.
- 7 years of experience coding in Python, Go, C++, or other programming languages.
- Experience with automation engineering, tooling, and data pipelines to support engineering workflows.
- Strong knowledge of GPU/accelerator architecture as it relates to AI workloads.
- Practical statistical knowledge applicable to performance analysis and forecasting.
- Excellent communication skills and ability to turn data into clear guidance for infrastructure teams and capacity planners.
- Experience with performance benchmarking and methodologies for AI/ML inference systems.
- Familiarity with capacity planning and forecasting/projection models for large-scale infrastructure.
- Experience with GPU profiling and observability tools (e.g., Nsight, other vendor-specific profilers).
- Experience with data visualization and reporting tools/frameworks for surfacing performance trends to stakeholders.
- Familiarity with ML serving frameworks and runtimes (e.g., Triton, TensorRT-LLM, vLLM, or similar).
- Experience with CI/CD and workflow orchestration tools for building automated performance analysis pipelines.
- Knowledge of cluster schedulers and orchestration platforms (e.g., Kubernetes).
- Experience with metrics and logging tools (e.g., Prometheus, Grafana, Splunk).
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🇬🇧 English