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, LinkedIn, 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 (DataOps): Build and manage the DataOps 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).
• Cross-Functional Leadership: Partner closely with the company leadership, AI/ML Engineering, Product, and Supply Chain teams to translate model requirements into actionable data collection and preparation strategies, ensuring the data roadmap aligns with product development goals.
• Infrastructure & Tooling: Oversee the selection, implementation, and management of data infrastructure, storage solutions (e.g., data lakes, feature stores), and specialized tooling required for large-scale AI data management. Establish and maintain robust data infrastructure that ensures secure, scalable storage, organization, and utilization of training data. Define back-end engineering requirements when necessary, working with AI and web engineering leads to ensure seamless integration with product needs.
• Team Leadership: Recruit, mentor, and lead a high-performing team of data strategists, data engineers, and data quality analysts.
Required Qualifications & Experience 💡
• 7+ years of progressive experience in Data Strategy, Data Operations, or Data Science leadership roles, with a significant focus on data for AI/ML models.
• Proven Experience in Generative AI: Direct experience building and scaling the data pipelines and strategies required to support complex generative AI models (e.g., image, 3D, or text-to-design models) is highly preferred.
• Domain Expertise: Familiarity with the unique challenges of multi-modal data and datasets related to e-commerce, manufacturing, or product design (e.g., CAD, visual assets, material science data).
• Leadership & Execution: Demonstrated ability to transition from high-level strategy to hands-on execution, building robust, production-ready data systems from the ground up.
• Data Governance Acumen: Deep understanding of data quality principles, metadata management, and data ethics, especially concerning proprietary or intellectual property data.
• Technical Proficiency: Strong knowledge of modern data technologies (e.g., cloud data warehousing, ETL/ELT tools, distributed systems) and data science programming languages (Python, SQL).
• Efficiency Mindset: Seeks continuous improvement in process and efficiency via tools and automation, with demonstrated ability to deliver continuous improvement preferred.
• Design and Taste Sensibility: An eye for and appreciation of aesthetics. Have an intuitive understanding of what makes for high quality, tasteful, well crafted consumer products
• Education: Bachelor’s, MBA or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
Why Join arcade.ai? 🚀
You will be joining a high-growth company at the intersection of AI, design, and manufacturing. This is a foundational leadership role where your work will directly impact the core functionality and business success of the platform. You will have the autonomy to build the data organization and infrastructure necessary to support world-class generative AI.
Key Performance Indicators (KPIs) and Success Metrics 📈
Success in this role will be measured by the ability to deliver high-quality, high-utility data that accelerates and improves the performance of arcade.ai's generative AI models and product platform.
• Data Velocity & Volume:
Growth in Unique Maker Data: Increase in the volume and diversity of usable, structured product design data acquired and onboarded into the platform's feature stores.
Data Pipeline Latency: Reduction in the time required for raw maker data to be processed, labeled, and made available for model training (i.e., time-to-value).
• Data Quality & Trust:
Data Quality Score: Achieve and maintain a target score for data integrity, completeness, and accuracy across critical datasets.
Annotation Efficiency and Accuracy: Reduction in the cost and time of annotation operations while meeting a strict target for label quality (e.g., 99\% inter-annotator agreement).
Data Feature Store Utilization: Percentage of available, curated data features actively consumed by AI/ML engineering teams in production and experimentation.
• Operational Excellence:
Data System Uptime: Ensuring high reliability of all core data pipelines and infrastructure.
Compliance Score: Maintaining adherence to all data governance, security, and IP compliance standards.