Creative AI Research Project Lead

Contra; New York (NY); On-site; USD 150,000 - 200,000; Full time

Benefits offered by this job

Equity

Company laptop

401k matching

Job summary

Contra Labs seeks a veteran researcher to own end-to-end study programs, from design to deliverables, across client engagements in AI and creative tools. You will coordinate between clients, creative experts, and engineers to produce rigorous datasets and insightful readouts.

You will shape methodologies, supervise annotation and evaluation workflows, ensure data quality and inter-rater reliability, and translate findings into practical recommendations and workshops for stakeholders.

Qualifications

• 3+ years designing studies, running evaluations, analyzing data, and presenting findings.
• Experience with data labeling, annotation, human-in-the-loop systems, or AI and ML evaluation workflows.
• Track record managing several concurrent projects, including annotation, evaluation, or QA work.
• A real point of view on creative quality and what "good" looks like.

Responsibilities

• Define methodologies for annotation protocols and evaluation designs.
• Manage delivery: scope, staffing, quality routing, timelines, and client relationship.
• Ensure data quality and inter-rater reliability, with real-time course correction.
• Translate analysis into findings, recommendations, reports, and workshops.
• Develop scalable systems with engineering to speed and reliability.
• Draft case studies and benchmark publications.

Skills

Designing studies

Data labeling

Annotation

Human-in-the-loop systems

AI/ML evaluation workflows

Project management

Creative quality judgment

Education

Master's degree or PhD in relevant field

Job description

Contra Labs seeks a veteran researcher to own end-to-end study programs, from design to deliverables, across client engagements in AI and creative tools. You will coordinate between clients, creative experts, and engineers to produce rigorous datasets and insightful readouts.

You will shape methodologies, supervise annotation and evaluation workflows, ensure data quality and inter-rater reliability, and translate findings into practical recommendations and workshops for stakeholders.