AI Research Scientist, Hardware AI Systems

What you'll do

• Develop AI systems that generalize across hardware engineering contexts and different SoC programs without bespoke retraining.
• Research transfer and multi-task learning for engineering agents across various IP blocks, tools, and program generations.
• Develop metareasoning policies for budgeting EDA steps, selecting abstractions, and recovering from tool or data failures.
• Build cross-program benchmarks and datasets to identify generalization gaps and track progress over time.
• Collaborate with RL scientists on reward shaping and credit assignment for slow or heterogeneous feedback.
• Publish research at top venues and maintain internal technical standards for evidence-backed generalization claims.
• Create reusable models, benchmarks, and transfer protocols that compound across the company's product roadmap.

What we're looking for

• PhD in Computer Science, Machine Learning, or a related field is strongly preferred.
• Proven research experience in machine learning with a focus on transfer learning and meta-learning.
• Expertise in intelligent agents and long-horizon reinforcement learning (RL).
• Experience with Large Language Models (LLM) tool use, planning, or hierarchical control in real toolchains.
• Exposure to hardware development including RTL, verification, physical design, or bring-up.
• Ability to develop metareasoning policies for budgeting EDA steps and selecting abstractions.
• Ability to collaborate on reward shaping and credit assignment for slow or heterogeneous feedback.
• Ability to publish research at top venues and maintain internal technical standards for generalization claims.