AI Research Engineer - Chip Design

Apple

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About The Position

As a member of Apple’s ML group, the AI Research Engineer will research and build AI systems that change how Apple silicon is designed. The role focuses on open-ended problems where the right formulation may not yet be known and data may be scarce or noisy. The engineer will work closely with chip design teams to turn those problems into well-defined research questions and deliver results for real projects. Success is measured by whether models and agents improve chip Power, Performance, and Area (PPA) metrics and design turnaround.

The engineer will join a growing team of AI and software engineers working with Apple’s chip design teams. The work spans research, data development, experimentation, and evaluation, as well as collaboration to move promising prototypes into production design flows.

Requirements

• MSc with a research thesis, or PhD, in Computer Science, Electrical Engineering, Applied Mathematics, Physics, or a related quantitative field.
• A research track record demonstrating leadership in problem formulation and execution, through peer-reviewed publications, a substantial thesis, or equivalent work.
• Strong AI fundamentals and hands-on experience building, training, and evaluating deep learning models in Python, such as with PyTorch.
• Hands-on experience with generative AI, such as building and evaluating LLM-based applications or agentic workflows involving tool calling, multi-agent orchestration, or retrieval.
• Experience achieving reliable results with imperfect real-world data, including small datasets, noisy labels, missing values, or distribution shift, and sound judgment about whether results can be trusted.
• Ability to work independently on loosely defined problems and explain technical results clearly to AI specialists and hardware engineers.

Nice To Haves

• PhD in AI, Computer Science, Electrical Engineering, or a related field.
• Publications at top-tier AI or EDA venues, such as NeurIPS, ICML, ICLR, DAC, ICCAD, or MLCAD.
• Familiarity with chip design flows such as physical design, place and route, and static timing analysis, or a strong interest in learning the domain in depth.
• Experience adapting foundation models, such as through fine-tuning, distillation, or preference optimization; building evaluation frameworks for agents; or deploying LLM systems in production.
• Experience with one or more of graph neural networks, Bayesian optimization, active learning, reinforcement learning, or probabilistic modeling.
• Experience integrating AI models or agents into EDA tool flows through scripting (Python/TCL) or APIs.
• Experience taking research into production, with solid software engineering practices.

Responsibilities

• Research and develop AI methods for the physical design flow, from floorplanning and placement through routing and timing closure, combining predictive models, optimization, and generative AI.
• Build agentic workflows connecting LLMs to EDA tools, design data, and internal infrastructure to automate and accelerate flow setup, debugging, design exploration, and analysis.
• Work with design teams to define research problems, metrics, baselines, and success criteria.
• Develop robust AI methods for limited, noisy, or imbalanced data using domain knowledge and advanced learning techniques.
• Extract, validate, and clean useful data from EDA tool outputs and design databases; build datasets and benchmarks where they do not exist.
• Run careful experiments and evaluations for models and agents, and clearly report results, including what does not work.
• Collaborate with software engineers and design teams to bring promising prototypes into production flows and improve them based on feedback from real projects.
• Help shape research directions, follow AI and EDA literature, and bring relevant ideas to the team.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level