ML Data Operations Engineer

Quick Facts

• Role: ML Data Operations Engineer
• Focus: Internal ML data collection studies for consumer machine learning features

Description

The ML Data Operations group is seeking a Data Operations Engineer to support internal data collection efforts powering next-generation consumer machine learning features. You will work closely with scientists and engineers to plan, execute, and oversee the hands-on bring-up, execution, and quality of internal data collection studies. The role emphasizes developing a technical understanding of the ML experiments you support, along with day-to-day study logistics.

Responsibilities

• Plan, execute, and track internal ML data collection studies with researchers, engineers, and scientists
• Develop a working understanding of supported ML experiments (model objectives, data requirements, labeling, and evaluation criteria) to ensure dataset quality
• Bring up and maintain pre-release hardware and software platforms used for data collection, including troubleshooting and triage
• Create and maintain technical documentation for hardware/software platform setup, study protocols, and data handling procedures
• Manage day-to-day logistics for internal study sessions, including scheduling participants and configuring setups
• Collaborate with algorithm, infrastructure, and hardware/software teams to gather and validate data collection requirements
• Track and communicate study progress, blockers, participant throughput, and dataset status to cross-functional partners and stakeholders
• Identify workflow gaps and lead process improvement initiatives (define, document, and socialize)

Requirements

• Bachelor’s degree in HCI, Cognitive Science, Psychology, Engineering, Operations, or equivalent combination of education and relevant experience
• Experience supporting or executing human user studies, behavioral research, or data collection operations in academic or industry settings
• Track record partnering with ML engineers or researchers to define data requirements, quality standards, or collection specifications

Benefits

• Work in a highly collaborative, technically demanding cross-functional environment supporting consumer ML features
• Hands-on partnership with ML scientists and engineers to ensure dataset quality and reliable study execution