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Salary: USD 240,000 - 290,000 / annual
Snorkel AI is seeking a Staff Software Engineer to lead the technical strategy and architecture for the Expert Contributor (EC) Platform. This role sits at the intersection of human expertise and machine learning, enabling domain experts to provide high-signal data for training and refining state-of-the-art AI models.
You will own the end-to-end Expert Contributor lifecycle, designing and developing mission-critical systems for automated onboarding workflows, performance management, and contributor retention. Key responsibilities include building robust features and integrations with third-party assessment and Employer of Record systems, automating supply and allocation logic to manage high volumes of expert contributors across data collection projects, and prototyping scalable services that power complex data user management pipelines.
As a Staff-level engineer, you will set the long-term technical direction and strategy, owning the transition from scheduled releases to a robust continuous deployment framework. You will lead complex, multi-quarter architectural initiatives ensuring services are innovative, scalable, and resilient. You'll mentor and guide Senior and Mid-level engineers on technical design, best practices, and project execution across multiple teams.
You will partner closely with product managers, designers, and ML experts to create exceptional user experiences for data acquisition and refinement. Additional responsibilities include setting strategy and architecture for build systems, testing frameworks, and CI/CD pipelines, and partnering with cross-functional teams to improve dev-infra and internal tooling.
The role requires 8+ years of software engineering experience developing performant, scalable web application architectures, with 2+ years at Staff level or equivalent. You should have strong expertise in distributed systems, cloud platforms (AWS preferred), REST API design for internal services, and a track record of leading complex engineering initiatives. Experience with enterprise software products, data collection systems, or machine learning applications is essential. Nice-to-have skills include hyper-growth startup experience, AI development workflows, and prior Tech Lead Manager roles.
