ML Data Operations Engineer

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Company Overview

A leading global technology company is dedicated to creating innovative products, services, and customer experiences that transform industries and enrich the lives of millions of users worldwide. With a strong culture of innovation, collaboration, and excellence, the organization continuously pushes the boundaries of technology while maintaining a commitment to sustainability and delivering impactful solutions for customers around the globe.

The organization is seeking an ML Data Operations Engineer to support internal data collection initiatives that power next-generation consumer machine learning features. This role offers the opportunity to work closely with scientists, engineers, and researchers while developing a strong technical understanding of machine learning experiments and data collection processes.

The successful candidate will be responsible for the execution, oversight, and optimization of internal data collection studies. This position requires a combination of technical operations expertise, project coordination, problem-solving abilities, and collaboration across multiple technical disciplines in a fast-paced and highly innovative environment.

Key Responsibilities

• Plan, execute, and track internal machine learning data collection studies in collaboration with researchers, engineers, and scientists.
• Develop a working understanding of machine learning experiments, including model objectives, data requirements, labeling methodologies, and evaluation criteria to ensure dataset quality.
• Bring up, maintain, and troubleshoot pre-release hardware and software platforms used for data collection activities.
• Create and maintain technical documentation covering platform setup, study protocols, and data handling procedures.
• Manage daily logistics for internal study sessions, including participant scheduling, hardware configuration, software setup, and session execution.
• Collaborate with algorithm, infrastructure, hardware, and software teams to gather and validate data collection requirements.
• Monitor and communicate study progress, blockers, participant throughput, and dataset status to stakeholders.
• Identify workflow inefficiencies and proactively implement process improvements to enhance operational effectiveness.
• Ensure data collection activities meet quality, privacy, and operational standards.

Required Qualifications

• Bachelor’s degree in Human-Computer Interaction (HCI), Cognitive Science, Psychology, Engineering, Operations, or a related field, or an equivalent combination of education and experience.
• Experience supporting or executing user studies, behavioral research, or data collection operations in academic or industry environments.
• Proven experience collaborating with machine learning engineers or researchers to define data requirements, quality standards, and collection specifications.
• Strong organizational and project management skills with the ability to coordinate multiple activities simultaneously.
• Excellent written and verbal communication skills with the ability to collaborate across technical and non-technical teams.
• Strong attention to detail and the ability to identify data inconsistencies and quality issues during data collection activities.

Preferred Qualifications

• 10+ years of experience in research operations, data collection coordination, or a related technical operations role.
• Hands-on familiarity with machine learning data pipelines, annotation tools, and dataset management practices.
• Experience working directly with engineering and scientific teams in highly technical environments.
• Ability to interpret technical documentation, data schemas, and experiment specifications.
• Familiarity with handling sensitive participant data and maintaining compliance with privacy and consent requirements.
• Self-motivated and capable of managing multiple concurrent studies with minimal supervision.
• Strong analytical and problem-solving abilities with a focus on operational excellence and continuous improvement.

Disclaimer

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