In this role, you will build and lead our forward-deployed engineering (FDE) team, working directly with leading labs and enterprises to scope, build and deliver high quality datasets to support their most critical AI initiatives
You’ll lead a team that will own quality in the end-to-end data pipeline. This will include working with customers to define what “good” data looks like to implement the relevant workflows in their platform. You will design innovative ML approaches to enhance human-in-the-loop (HITL) techniques and improve the efficiency of data generation and review processes. Your team will own systems and tools that enable consistent, scalable, and high-quality data delivery to our customers
Sitting at the critical intersection of data engineering, ML engineering, operations, and customer engagement— leading scoping and preselling efforts. You'll also partner closely with the Snorkel delivery team and cross-functional stakeholders to define quality standards, develop measurement frameworks, drive ML-based workflows to improve data pipelines and unblock projects through technical innovation. As the founding member, you’ll also roll up your sleeves to define and own the workflows and processes that are needed to deliver exceptional data at scale
Build and lead the Forward Deployed Engineering DaaS organization, setting a clear vision, defining the operating model and scaling its impact across Snorkel’s Expert Data-as-a-Service workflows
Build, mentor, and motivate high performing teams, including cultivating skills and culture needed to consistently deliver exceptional outcomes and transformative impact
Own and evolve the data pipeline components of the DaaS stack, including model-assisted labeling and data generation, quality estimation, and data-centric feedback loops that guide human input
Partner with customers - including research and engineering teams at Frontier AI Labs - to scope requirements for complex, novel AI datasets and translate needs into delivery-ready workflows
Develop robust systems for request intake, task orchestration, SLA tracking, and progress monitoring to ensure seamless execution and prevent critical delivery gaps
Collaborate cross-functionally with research and engineering teams to innovate, develop, and productionize HITL data generation methods, advanced quality techniques, and improve internal delivery tooling
Drive continuous improvement by developing reusable workflows, surfacing operational insights, and enabling the organization to scale faster while maintaining high quality