Applied AI Engineer, Product Convergence and Closure

We're rebuilding our toolchain around AI to optimize chip behaviour. As an Applied AI Engineer, you'll build infrastructure that turns raw simulation data into real firmware tuning, product specs, and manufacturing limits. You'll use LLMs and agents to automate analysis, validation, and reporting work.

Key responsibilities:

• Build pipelines between tools to turn raw simulation data into real firmware tuning, product specs, and manufacturing limits.
• Use LLMs and agents to automate analysis, validation, and reporting work.
• Build observability and validation systems to catch data errors and inconsistencies.
• Work with product convergence, silicon architecture, firmware, and manufacturing teams to translate new hardware requirements and capabilities into workflows.

Requirements:

• BS/MS in CS, CE, EE, or Systems Engineering, or equivalent experience.
• 4+ years shipping production Python services and data pipelines.
• Hands-on experience applying LLMs to engineering problems.
• Strong instincts for data quality.
• Ability to keep up with a fast-paced AI landscape.

Nice to have:

• Silicon product proficiency.
• MCP, DSPy, or LLM evaluation frameworks.
• Perl interop for legacy chip-data workflows.
• Crafting dashboards and visualizations for diverse collaborators.