Staff Machine Learning Engineer

3 Months ago • All levels • Research Development • $175,800 PA - $312,200 PA

Job Summary

Job Description

The ADP ML Data Platform team at Apple is seeking a Staff Machine Learning Engineer to develop and optimize machine learning technologies. Responsibilities include prototyping and optimizing GenAI models, building platforms for model configuration and deployment, and improving platform capabilities for next-generation ML workloads. The role involves using 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 to ensure maximum uptime and performance.
Must have:
  • Prototype and optimize GenAI models for scalable production use.
  • Build platform for model configuration, tuning and deployment.
  • Improve platform capabilities for next-gen ML workloads.
  • Use ML techniques to drive smarter data workflows.
  • Collaborate with research and engineering teams.

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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