12 - Principal, Design Engineering

Celestica International LP•San Jose, CA

11d•$190,000 - $270,000•Onsite

About The Position

This role focuses on leading the hands-on design and coding of AI architectures, specifically RAG (Retrieval-Augmented Generation) and agentic workflows. The objective is to build systems that enable hardware engineers to query complex design rules and legacy data with high accuracy. The position involves writing custom Python scripts for data extraction from various engineering sources, optimizing foundation models for hardware design, developing AI capabilities for interpreting visual engineering data, implementing predictive analytics for defect detection, and managing vector databases for efficient retrieval of design rules. The role also includes technical leadership within the engineering organization, managing relationships with AI research partners and cloud vendors, researching emerging AI/ML trends, designing systems for efficiency and customer standards, liaising with stakeholders, and recommending system modifications based on user feedback.

Requirements

The "Builder" DNA: Strong evidence of building and deploying AI-powered applications.
Python Mastery: Ability to write clean, scalable, and efficient Python code.
Data Wrangling: Ability to extract signals from unstructured data, including complex technical PDFs and log files.
Product Sense: Demonstrated ability to turn user requirements into functional AI features.

Nice To Haves

Hardware Curiosity: Interest in PCBs, schematics, and manufacturing processes.
Knowledge in Hardware design tools e.g: Cadence Allegro, Siemens
Cloud Fluency: Experience with Google Vertex AI or similar enterprise platforms.
Background in Complex Systems for e.g: Experience in datacenter solutions for Hyperscalers
Advanced Degrees: MS or PhD in a relevant field (EE, Computer engineering, Computer Science) or a strong portfolio of technical leadership.

Responsibilities

Lead the hands-on design and coding of RAG (Retrieval-Augmented Generation) architectures and agentic workflows.
Build systems that allow hardware engineers to "query" complex design rules and legacy data with high accuracy.
Write custom Python scripts and parsers to extract structured intelligence from diverse sources, including PDF datasheets, EDA output files, Netlists, and complex block diagrams.
Execute fine-tuning runs for foundation models (e.g., Gemini/Vertex AI) using proprietary historical data to create a domain-specific expert model for hardware design.
Develop capabilities for AI to interpret visual engineering data, such as thermal heatmaps, mechanical drawings, and circuit diagrams, to automate technical documentation and manuals.
Implement "Shift-Left" algorithms that utilize historical yield and manufacturing data to predict potential defects during the early stages of the design cycle.
Build and optimize the vectorization pipeline (using Pinecone, Milvus, or similar) to ensure the AI can retrieve specific design rules without "hallucinations."
Act as a subject matter expert (SME) for AI/ML within the engineering organization, providing guidance on tool integration and automating workflows within EDA suites.
Manage relationships with key AI research partners and cloud vendors.
Research emerging AI/ML trends and develop implementation plans for the engineering roadmap.
Design and implement systems that meet both internal efficiency goals and customer-specific design standards.
Liaise with hardware SMEs, software teams, and external contractors to ensure tool adoption.
Recommend system modifications based on user feedback from the engineering floor.