12 - Principal, Design Engineering

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Childcare support

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Learning budget

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Verification

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Salary

$190K-$270K not verified - source not recorded; timestamp not recorded

401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

105% above the BLS role benchmark for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

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Weekend work

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Application

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Assessment

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Deadline

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Where they hire

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About this role

12 - Principal, Design Engineering City San Jose | State/Province California | Country USA Req ID: 131895 Region: Country USA State/Province: State/Province California City San Jose Detailed Description - AI Architecture & Development: 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. - Engineering Data Strategy: Write custom Python scripts and parsers to extract structured intelligence from diverse sources, including PDF datasheets, EDA output files, Netlists, and complex block diagrams. - Model Optimization & Tuning: 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. - Multimodal Analysis: Develop capabilities for AI to interpret visual engineering data, such as thermal heatmaps, mechanical drawings, and circuit diagrams, to automate technical documentation and manuals. - Predictive Analytics: Implement "Shift-Left" algorithms that utilize historical yield and manufacturing data to predict potential defects during the early stages of the design cycle. - Vector Database Management: Build and optimize the vectorization pipeline (using Pinecone, Milvus, or similar) to ensure the AI can retrieve specific design rules without "hallucinations." - Technical Leadership: Act as a subject matter expert (SME) for AI/ML within the engineering organization, providing guidance on tool integ

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