Remote AI Research & Evaluation Lead

24-MAG; United States; Remote; USD 600,000 - 2,000,000; Full time

Benefits offered by this job

Fully remote

Job summary

24-MAG LLC offers a full-time, fully remote opportunity at the intersection of AI research and real-world model performance. This role leads research evaluation, ML-oriented data design, failure analysis, and quality calibration to drive defensible signals and measurable improvements in AI systems.

The ideal candidate combines strong judgment on signal quality with experience in constructing evaluation frameworks and QA processes, while communicating findings clearly to stakeholders across

Qualifications

• Strong judgment on research signal quality and readiness to support conclusions.
• Experience designing ML-oriented datasets and evaluation frameworks.
• Ability to translate complex real-world behavior into structured research opportunities.
• Comfort making decisions in uncertain, evolving environments.
• Excellent written and verbal communication skills.

Responsibilities

• Own research and evaluation initiatives from framing to data design and signal validation.
• Define rigorous approaches to ensure reliable, defensible research signal.
• Analyze model and system failures to identify root causes and improvements.
• Evaluate datasets, experiments, and conclusions against quality thresholds.
• Collaborate with researchers, domain experts, and operators to align efforts.

Skills

Research signal quality

ML evaluation frameworks

Data design

Quality assurance

Communication skills

Systems-level thinking

Job description

24-MAG LLC offers a full-time, fully remote opportunity at the intersection of AI research and real-world model performance. This role leads research evaluation, ML-oriented data design, failure analysis, and quality calibration to drive defensible signals and measurable improvements in AI systems.

The ideal candidate combines strong judgment on signal quality with experience in constructing evaluation frameworks and QA processes, while communicating findings clearly to stakeholders across