About the job
This is a horizontal, high-impact L6 Staff Fullstack Engineer & Architect position reporting directly to the Director of Contributor Engineering. Instead of being tied to a single domain, your scope is spread across all Contributor (CB) teams (including Allocation, Growth, Trust & Safety, Pay, and Allocations). Together, these teams power Scale’s AI data operations - from building high-impact datasets that push the boundaries of LLM capabilities, to optimizing contributor onboarding and incentives, to safeguarding data integrity through advanced trust, safety, and security measures. They work at the intersection of ML, operations, and analytics to ensure we deliver the highest-quality data at scale. You will act as an organizational architect and tech lead, dynamically embedding yourself into the highest-priority projects across the org to guarantee execution, unblock teams, and successfully ship mission-critical initiatives. Concurrently, you will lead the long-term technical evolution of our stack, transforming the core architecture to ensure it is highly sustainable, scalable, and fundamentally AI-native.
Responsibilities
Deploy flexibly into critical, fast-moving product initiatives across the CB organization
Lead the architectural overhaul of our platform infrastructure, making it highly sustainable, robust, and optimized for deep integration with LLMs and foundation models.
Lead architecture decisions for scalability, reliability, and performance
Mentor and uplevel engineers across the team
Partner with product and leadership to shape roadmap and priorities
Own large, ambiguous problem spaces end-to-end
Work across backend, frontend, and ML systems
Qualifications
Minimum
7+ years of full-time engineering experience, post-graduation, with a proven track record of operating as a Tech Lead, Architect, or Principal Engineer.
Track record of shipping high-quality products and features at scale
Proficient in Javascript/Typescript, and SQL
Experience with Kubernetes
Experience with major cloud providers (AWS, Azure, GCP)
Preferred
Experience tinkering with or productizing LLMs, vector databases, and the other latest AI technologies