Staff Machine Learning Engineer

2 Months ago • All levels • Research Development • $166,600 PA - $296,300 PA

Job Summary

Job Description

The ADP ML Data Platform team at Apple is seeking a Staff Machine Learning Engineer to enable future intelligent products. The role involves prototyping and optimizing GenAI models, building a platform for easy model configuration and deployment, and continuously improving platform capabilities for next-gen ML workloads. Responsibilities include utilizing ML techniques for smarter data workflows, collaborating with research and engineering teams, and optimizing platform components for large-scale ML workloads. The engineer will also diagnose and fix complex issues across the entire stack. This role offers the opportunity to contribute to cutting-edge ML technologies and systems.
Must have:
  • Prototype and optimize GenAI models for scalable production use
  • Build platform to easily configure models and ensure deployment.
  • Improve platform capabilities for next-gen ML workloads.
  • Use ML techniques to drive smarter data workflows.
  • Collaborate across teams to accelerate experimentation.
  • Optimize platform components for large-scale ML workloads.

Job Details

The ADP ML Data Platform team enables future Apple intelligent products by providing Apple engineers with cutting edge ML technologies, large scale compute and data systems specifically designed for machine learning. As a member of the Apple ML Data Platform team, your responsibilities will include: * Prototype and optimize GenAI models, including open-source models, for scalable production use * Build a platform that enables teams to easily configure models, apply tuning strategies (e.g., LoRA/QLoRA), perform quantization, and get models production-ready for scalable deployment * Continuously improve platform capabilities to handle next-gen ML workloads, including foundation models and retrieval-augmented systems * Use ML techniques to drive smarter data workflows - including synthetic data generation, automated labeling, active learning, and data curation * Collaborate across research and engineering teams to accelerate experimentation * Collaborate closely with teams across the stack to enable high-quality, end-to-end ML experiences * Use and extend tools built on modern ML frameworks * Optimize platform components for large-scale ML workloads across distributed systems * Diagnose, fix, improve, and automate complex issues across the entire stack to ensure maximum uptime and performance

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