AI Research Engineer - Chip Design

Apple | Consumer Electronics

4h 59m ago

On-site

Full Time

Summary

Posted: Nov 03, 2026

Role Number:200693100-0865

As a member of our ML group, you will research and build AI systems that change how Apple silicon is designed. Many of these problems are still open: the right way to frame the problem isn't known yet, and the data is scarce or noisy.

Description

We are looking for a strong researcher who enjoys working closely with design teams to turn open-ended questions into well-posed research problems, and who wants to see the results used in real projects. Success in this role means models and agents that measurably improve designed chip metrics (Power, Performance, and Area) and design turnaround. You will join a growing team of AI and software engineers working with Apple's chip design teams.

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
Design and build agentic workflows that connect LLMs to EDA tools, design data, and internal infrastructure, to automate and speed up tasks such as flow setup, debug, design exploration, and analysis
Work with design teams to turn open-ended problems into well-defined research questions with clear metrics, baselines, and success criteria
Develop robust AI methods for limited, noisy, or imbalanced data, leveraging domain knowledge and advanced learning techniques
Own data end to end: extract useful signal from EDA tool outputs and design databases, validate and clean it, and build datasets and benchmarks where none exist
Run careful experiments and evaluations for both models and agents, and report clearly what works and what doesn't
Work with software engineers and design teams to bring promising prototypes into production flows, and improve them based on feedback from real projects
Help shape new research directions as our work grows, follow the AI and EDA literature, and bring relevant ideas to the team

Minimum Qualifications

MSc with a research thesis, or PhD, in Computer Science, Electrical Engineering, Applied Mathematics, Physics, or a related quantitative field
Research track record in which you led both problem formulation and execution, shown 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 (e.g., PyTorch)
Hands-on experience with generative AI, such as building and evaluating LLM-based applications or agentic workflows (e.g., tool calling, multi-agent orchestration, retrieval)
Experience getting reliable results from imperfect real-world data (small datasets, noisy labels, missing values, distribution shift), with sound judgment about when a result can be trusted
Ability to work independently on loosely defined problems, and to explain technical results clearly to both AI specialists and hardware engineers

Preferred Qualifications

PhD in AI, Computer Science, Electrical Engineering, or a related field
Publications at top-tier AI or EDA venues (e.g., NeurIPS, ICML, ICLR, DAC, ICCAD, 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 (e.g., fine-tuning, distillation, 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, 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

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Required Skills

pytorchpythontcl

Required Languages

🇬🇧 English

Description

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
Design and build agentic workflows that connect LLMs to EDA tools, design data, and internal infrastructure, to automate and speed up tasks such as flow setup, debug, design exploration, and analysis
Work with design teams to turn open-ended problems into well-defined research questions with clear metrics, baselines, and success criteria
Develop robust AI methods for limited, noisy, or imbalanced data, leveraging domain knowledge and advanced learning techniques
Own data end to end: extract useful signal from EDA tool outputs and design databases, validate and clean it, and build datasets and benchmarks where none exist
Run careful experiments and evaluations for both models and agents, and report clearly what works and what doesn't
Work with software engineers and design teams to bring promising prototypes into production flows, and improve them based on feedback from real projects
Help shape new research directions as our work grows, follow the AI and EDA literature, and bring relevant ideas to the team

Minimum Qualifications

MSc with a research thesis, or PhD, in Computer Science, Electrical Engineering, Applied Mathematics, Physics, or a related quantitative field
Research track record in which you led both problem formulation and execution, shown 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 (e.g., PyTorch)
Hands-on experience with generative AI, such as building and evaluating LLM-based applications or agentic workflows (e.g., tool calling, multi-agent orchestration, retrieval)
Experience getting reliable results from imperfect real-world data (small datasets, noisy labels, missing values, distribution shift), with sound judgment about when a result can be trusted
Ability to work independently on loosely defined problems, and to explain technical results clearly to both AI specialists and hardware engineers

Preferred Qualifications

PhD in AI, Computer Science, Electrical Engineering, or a related field
Publications at top-tier AI or EDA venues (e.g., NeurIPS, ICML, ICLR, DAC, ICCAD, 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 (e.g., fine-tuning, distillation, 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, 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