About Scale At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel advancements in AI, including generative AI, defense applications, and autonomous vehicles. With our recent Series F funding, we are expanding our frontier data capabilities to pave the road to Artificial General Intelligence (AGI), building on our model evaluation work with enterprise customers and governments to enhance our offerings for both public and private evaluations. About Data Engine Our Generative AI Data Engine powers the world’s most advanced LLMs and generative models through RLHF (Reinforcement Learning with Human Feedback), human data generation, model evaluation, safety, and alignment. The data we produce is critical for how humanity will interact with AI. About the Teams During the interview process, you'll be considered for opportunities across several teams within the GenAI Engineering organization, based on your interests, expertise, and business needs. Frontier Data: Builds high-impact datasets that push LLM capabilities, working closely with customers, GTM, and operations to enable advanced reasoning and agentic workflows. Leads ambitious projects and sets industry standards for AI data quality. Growth: Manages the contributor platform experience, developing tools for onboarding, project matching, assessments, and incentives. Uses data science to grow the contributor base and improve the data labeling ecosystem. Trust & Safety: Ensures data integrity by detecting and preventing fraud, cheating, and abuse, combining ML, analytics, and security to deliver accurate, secure, and high-quality data. Pay, Incentives & Allocation: Ensures fair contributor pay, effective incentives, and proper project matching. Oversees pay rates, fulfillment, and recommendation engines to enhance contributor satisfaction and data delivery. Responsibilities: Design, build, and maintain scalable systems across full stack, including front-end, back-end, and infrastructure layers. Implement features using TypeScript, React, Node.js, MongoDB, Elasticsearch, and Temporal. Collaborate with internal operators to identify bottlenecks and ship pragmatic solutions quickly. Own core systems impacting the contributor platform, data pipeline, and business outcomes. Architect infrastructure to handle millions of tasks weekly with high reliability and low latency. Partner with ML teams, engineers, and product teams to ensure data quality and operational excellence. Promote engineering best practices through mentorship, code reviews, and process improvements.

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