AI Analyst | Remote

Crossing Hurdles; United States; Remote; USD 240,000 - 350,000; Full time

Job summary

Crossing Hurdles is seeking a Member of Technical Staff for Frontier AI. This remote, full-time role centers on leading end-to-end research and evaluation, designing ML-oriented data systems, and validating signals that improve model performance.

You will work with researchers to calibrate quality early and push for decisive action in ambiguous environments. You will design datasets, evaluation schemas, and QA processes, translating real-world behavior into structured opportunities while

Qualifications

• Strong judgment around research signal quality and when work is externalized.
• Experience designing ML-oriented datasets, evaluation frameworks, and QA processes.
• Ability to translate messy, real-world system behavior into structured research opportunities.
• Comfort operating in ambiguity with ownership and decisive action.
• Excellent written and verbal communication, especially about tradeoffs and signal strength.

Responsibilities

• Own end-to-end research and evaluation initiatives: framing, data design, calibration, and signal validation.
• Design ML-oriented data systems with task definitions, annotation schemas, rubrics, incentives, and pipelines.
• Analyze model and system failures to identify root causes and opportunities for improvement.
• Translate ambiguous behavior into structured evaluation frameworks and data categories.
• Collaborate with researchers to calibrate quality early and raise signal strength.
• Iterate on evaluations, datasets, and feedback loops to improve system performance.

Skills

Research signal quality

ML data systems design

Evaluation frameworks

QA processes

Clear communication

Job description

Position: Member of Technical Staff, Frontier AI

Type: Full time

Compensation: $240,000 $350,000

Location: Remote

Commitment: 10-40 hrs/week

Role Responsibilities

• Own research and evaluation initiatives end-to-end: problem framing, data design, quality calibration, and signal validation.
• Design ML-oriented data systems, including task definitions, annotation schemas, rubrics, incentives, and pipelines optimized for downstream model performance.
• Analyze model and system failures to identify root causes, edge cases, and opportunities for improvement.
• Translate ambiguous, real-world behavior into structured evaluation frameworks and new data categories.
• Work closely with researchers and domain experts to calibrate quality early and continuously raise the signal bar.
• Iterate rapidly on evaluations, datasets, and feedback loops to improve system performance.

Requirements

• Strong judgment around research signal quality and when work is ready to be externalized.
• Experience designing ML-oriented datasets, evaluation frameworks, and QA processes.
• Ability to translate messy, real-world system behavior into structured research and evaluation opportunities.
• Comfort operating in ambiguity, with a bias toward ownership and decisive action.
• Clear written and verbal communication, especially when explaining tradeoffs, limitations, and signal strength to stakeholders.