AI Research Engineer - Chip Design

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.

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.

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