Data Flywheel Architect for AI Hardware

Normal Computing; New York (NY); On-site; USD 140,000 - 210,000; Full time

Job summary

Normal Computing in New York designs silicon that turns thermal noise into a computational resource and builds AI-native EDA tools that scale performance and efficiency. You will own end-to-end data generation pipelines, mine and curate synthetic training data, and partner with ML/post-training and eval teams to drive measurable improvements.

You will negotiate data access with customers, handle redaction and IP constraints, and establish data governance and lineage as the data strategy scales.

Qualifications

• Experienced in building and operating data pipelines for ML training data.
• Ability to evaluate data quality, detect noise and gaps.
• Strong collaboration across ML, verification, and data engineering teams.

Responsibilities

• Own the data pipeline end to end, from data generation to curation and ingestion.
• Negotiate data access with customers, including redaction and IP considerations.
• Build external replicas of customer environments while preserving structure of specs/testbenches.

Skills

Data pipelines

ML data generation

Data acquisition

Performance measurement

Tools

SystemVerilog

Verilog

UVM

Job description

Normal Computing in New York designs silicon that turns thermal noise into a computational resource and builds AI-native EDA tools that scale performance and efficiency. You will own end-to-end data generation pipelines, mine and curate synthetic training data, and partner with ML/post-training and eval teams to drive measurable improvements.

You will negotiate data access with customers, handle redaction and IP constraints, and establish data governance and lineage as the data strategy scales.