Enterprise AI Field Engineer — Deploy & Optimize ML

Drive Capital; San Francisco (CA); Hybrid; USD 180,000 - 325,000; Full time

Job summary

Drive Capital is seeking a Forward Deployed AI Engineer to embed within enterprise silicon design environments. This role involves adapting Normal EDA to customer data and workflows, ensuring ML systems deliver high-quality performance.

The ideal candidate has a background in hardware engineering or semiconductor verification, strong software skills in Python, and experience with ML systems. The position offers competitive compensation ranging from $180K to $325K and a hybrid work model.

Qualifications

• Experience shipping ML systems in production environments.
• Interest or background in semiconductor verification or hardware engineering.
• Ability to deep dive into verification workflows like UVM or SystemVerilog.
• Strong software engineering fundamentals.
• Hands-on experience with the modern ML stack.

Responsibilities

• Adapt Normal EDA to customer workflows and proprietary data.
• Validate generated artifacts against specifications.
• Bridge the gap between customer engineering and ML systems.
• Make judgment calls to balance customer requests with quality.
• Codify successful engagement patterns for future use.

Skills

Shipping ML systems

Understanding hardware engineering

Proficiency in Python

Experience with modern ML stack

Ability to work in ambiguous situations

Tools

SystemVerilog

UVM

Job description

Drive Capital is seeking a Forward Deployed AI Engineer to embed within enterprise silicon design environments. This role involves adapting Normal EDA to customer data and workflows, ensuring ML systems deliver high-quality performance.

The ideal candidate has a background in hardware engineering or semiconductor verification, strong software skills in Python, and experience with ML systems. The position offers competitive compensation ranging from $180K to $325K and a hybrid work model.