SoC Machine Learning Design Engineer

Apple Inc.
Cupertino, United States of America
14 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

Cupertino, United States of America

Tech stack

Artificial Intelligence
Apple Products
Artificial Neural Networks
Hardware Description Language
Machine Learning
TensorFlow
Static Timing Analysis
System on a Chip
SystemVerilog
Verilog
VHDL
AI Infrastructure
Reinforcement Learning
PyTorch
Large Language Models
Deep Learning
Generative AI
Artificial Intelligence Markup Language (AIML)
Data Pipelines

Job description

At Apple we believe our products begin with our people. By hiring a diverse team, we drive creative thought. By giving that team everything they need, we drive innovation. By hiring incredible engineers, we drive precision. And through our collaborative process, we build memorable experiences for our customers! These elements come together to make Apple an amazing environment for motivated people to do the greatest work of their lives. You will become part of a hands-on development team that sets the standard in cultivating excellence, creativity and innovation. Come help us design the next generation of revolutionary Apple products. We are looking for a forward-thinking and unusually talented engineer. As a member of our dynamic group, you will have the rare and rewarding opportunity to craft and implement methodologies and solutions with a high impact on upcoming products that will delight and inspire millions of Apple's customers every single day. In this role, you will be directly involved in our SoC design machine learning efforts, collaborating right alongside our internal multi-functional teams, and using your expertise in machine learning to improve productivity and optimizations across several SoC design related functions spanning from design to validation., * As a member of the SoC design machine learning team, you will be part of a dynamic team that is building the most efficient application processors on the planet, powering the next generation of Apple products.

  • Your expertise in machine learning will be instrumental in optimizing for efficiency, quality and speed for our chip-design process.
  • You'll play a crucial role in developing generative AI and machine learning solutions for optimizations in RTL Design, Verification, and Power/Performance/Area efforts.
  • You will collaborate closely with our internal multi-functional teams as well as the AIML organization at Apple to understand domain-specific needs and tailor machine learning solutions to these domains.
  • As part of this role, you will keep abreast of emerging technologies in machine learning and chip design to ensure our solutions remain state-of-the-art.
  • Prior leadership experience a plus.

Requirements

  • Minimum of BS + 10 years relevant industry experience., * Practical experience and knowledge of generative AI and modern machine learning methods.
  • Experience with any of the following: RAG systems including embedding models, retrieval strategies, and context optimization techniques. Generative AI pipeline development. AI evaluation and testing proficiency including designing test suites and implementing human evaluation frameworks. Experience with AI coding assistants. Experience with AI infrastructure protocols such as MCP.
  • Experience with Deep neural networks and reinforcement learning is a plus
  • Solid math background and understanding of algorithms and data structures
  • Experience with current deep learning frameworks, such as PyTorch, TensorFlow, JAX or MLX
  • Experience with hardware description languages like Verilog, SystemVerilog or VHDL.
  • Experience with Chip design and verification front end flows is a plus: Working verification experience with UVM testbenches. Working experience with front-end tools such as Static timing analysis and CDC/RDC.
  • Strong communication and collaboration skills, with the ability to work efficiently in cross-functional teams
  • Master's or PhD with relevant publications preferred but not required.

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