AI Data Operations Lead — Scale Pipelines & Quality

E-Solutions; Mountain View (CA); On-site; USD 120,000 - 190,000; Full time

Job summary

E-Solutions in Mountain View, CA is seeking a Data Operations Lead to own and execute the data strategy for research teams advancing frontier AI capabilities, including RLHF, safety, tool use, and agentic workflows. You will shape data collection at scale and partner with senior researchers to map requirements to data solutions.

You will drive measurable improvements in data quality, build scalable pipelines, and establish evaluation frameworks.

Qualifications

• 3+ years in operations, consulting, PM, or program management.
• Excellent project management across multiple complex projects.
• Strong communication with technical and non-technical stakeholders.
• Familiar with LLMs and AI training methodologies.
• Highly organized and able to navigate ambiguity.
• Experience with data analysis tools (SQL, Python, Tableau) or similar.
• Thrives in fast-paced research environments.

Responsibilities

• Own and execute data strategy for research teams advancing frontier AI capabilities.
• Drive strategic vendor partnerships and build scalable frameworks for data collection at scale.
• Design and implement operational systems translating research requirements into data pipelines.
• Build evaluation frameworks and quality standards to train state-of-the-art AI systems.
• Lead cross-functional initiatives to optimize research velocity while maintaining quality.
• Identify risks, bottlenecks, and opportunities to improve efficiency across data operations.
• Partner with senior research leaders to align data operations with model development roadmaps.

Skills

Project management

Stakeholder communication

SQL

Python

Tableau

Data analysis

Job description

E-Solutions in Mountain View, CA is seeking a Data Operations Lead to own and execute the data strategy for research teams advancing frontier AI capabilities, including RLHF, safety, tool use, and agentic workflows. You will shape data collection at scale and partner with senior researchers to map requirements to data solutions.

You will drive measurable improvements in data quality, build scalable pipelines, and establish evaluation frameworks.