Lead, Data Operations & Evaluation Engineering

About Arcade

Arcade is building the world’s first AI physical product creation platform, where imagination becomes reality. Our platform lets anyone design, purchase, and sell custom, manufacturable products using natural language and generative AI. We believe everyone should have the power to create physical goods as easily as they post online, and we’re building the infrastructure to make that real for both consumers and businesses.

We’ve raised $42M from a world‑class group of investors, including Reid Hoffman, Forerunner Ventures (Kirsten Green), Canaan Partners (Laura Chau), Adverb Ventures (April Underwood), Factorial Funds (Sol Bier), Offline Ventures (Brit Morin), Sound Ventures (Ashton Kutcher), Inspired Capital (Alexa von Tobel), and Torch Capital (Jonathan Keidan). Our angel investors include Elad Gil, Ev Williams, Marissa Mayer, Sara Beykpour, Kayvon Beykpour, Anna Veronika Dorogush, Eugenia Kuyda, David Luan, Sharon Zhou, Kelly Wearstler, Karlie Kloss, Colin Kaepernick, Christy Turlington Burns, and Jeff Wilke.

Arcade

is headquartered in San Francisco’s Presidio and led by serial entrepreneur Mariam Naficy (Minted, Eve), and a mission-driven team from Google, Apple, Stability AI, Glean, NVIDIA, Databricks, Linked In, Stanford, MIT, Berkeley, and more. We’re pioneering a new category at the intersection of AI, personal expression, and on‑demand manufacturing, and we’re building fast. 🌟 Lead, Data Operations & Eval Engineering

Arcade is revolutionizing e-commerce by building a generative AI design platform where consumers, makers, and enterprises can design any product imaginable, instantly connecting it to a streamlined supply chain.

Data is the lifeblood of our platform, powering the next generation of creative product design. We are looking for an exceptional technical lead to establish and execute our company‑wide AI data strategy and operations, focused on
1) the sourcing, production, organization, and processing of diverse data to fuel our generative AI models, and
2) the metrics, tooling, and process for evaluating our AI models.

🎯 Key Responsibilities

• Develop and Execute AI Data Strategy:
  Define and lead the comprehensive data strategy for arcade.ai, ensuring the collection, curation, and governance of unique, vast, and diverse datasets (e.g., jewelry specifications, home decor designs, material properties) are optimized for generative AI model development and training.
• AI Data Acquisition & Management:
  Define training data requirements in partnership with the AI team and CEO to support model development and research objectives. Lead and implement acquisition strategy including both original data production as well as strategic partnerships. Drive execution of data organization and acquisition plans.
• Establish Data Operations (Data Ops):
  Build and manage the Data Ops function, creating scalable, automated, and quality‑controlled pipelines for data ingestion, cleaning, normalization, enrichment, and labeling, specifically around complex, multi‑modal product design data.
• Lead Data Annotation Operations:
  Design, implement, and oversee highly efficient and quality‑focused data annotation pipelines for complex data types (e.g., semantic segmentation, feature labeling, quality scoring) critical for training generative design models. Manage vendor relationships or internal teams dedicated to annotation.
• Oversee Model Evaluation Data:
  Collaborate closely with the AI/ML Engineering team to establish and manage the datasets, metrics, and processes used for continuous model evaluation and testing (A/B testing, human‑in‑the‑loop validation, performance benchmarking) to ensure the generated designs meet quality and utility standards.
• Perform AI Evaluation Engineering:
  Design and build labeling pipelines, human‑in‑the‑loop evaluations, automated evaluation, and eval metrics. Build labeling tools, craft eval suites (e.g. CLIP similarity, detection accuracy), manage datasets.
• Data Governance & Quality:
  Implement best‑in‑class data governance policies, standards, and procedures to ensure data accuracy, consistency, security, and compliance (e.g., intellectual property rights for makers’ designs).
• Cro…