Foundational Data Engineer

About the Company

Normal Computing builds silicon that turns thermal noise from an obstacle into a computational resource. We co-design the full stack: AI-native EDA systems in production with the world's largest semiconductor companies, and the advanced ASICs they make possible.

Responsibilities

• Improve models for hardware design, verification, and EDA workflows
• Own the data flywheel including rejection sampling, distillation, and mining eval-passing trajectories
• Identify, evaluate, and acquire datasets relevant to hardware design and EDA workflows
• Partner with verification engineers to define quality rubrics and curate golden reference examples
• Operate data ingestion pipelines and maintain structured catalogs of data sources and lineage
• Negotiate customer-data access and build external replicas of customer environments to preserve structure without exposing IP
• Manage engineers across synthetic data, verification SME curation, and data infrastructure as the team scales

Requirements

• Experience building or using a data flywheel (model outputs curated into training rounds)
• Proven track record of shipping synthetic-data or training-data pipelines that produced measurable model improvement
• Ability to evaluate data quality independently, spotting noise, bias, and gaps
• Experience approaching data acquisition as a systematic, engineering-driven problem
• Strong organizational skills with a focus on data provenance and lineage

Preferred Qualifications

• Experience managing vendor relationships for data acquisition
• Familiarity with SystemVerilog, Verilog, and UVM
• Background in code-model or agent training-data pipelines
• Experience with automated data collection, web scraping, or corpus curation at scale
• Prior experience in a startup or fast-moving research environment