Frontier AI Research Engineer (Remote)

Crossing Hurdles; United States; Remote; USD 600,000 - 2,000,000; Full time

Job summary

Crossing Hurdles is seeking a Member of Technical Staff, Frontier AI, for remote collaboration. The role focuses on owning research and evaluation initiatives across problem framing, signal validation, and data-system design to drive robust AI capabilities.

You will design ML-oriented datasets and evaluation frameworks, translate system behavior into research opportunities, and work with researchers to raise the signal strength while owning end-to-end evaluation loops.

Qualifications

• Strong judgment around research signal quality and readiness for externalization.
• Experience designing ML-oriented datasets and evaluation frameworks.
• Ability to translate real-world system behaviour into structured research opportunities.
• Comfort operating in ambiguity with ownership and decisive action.

Responsibilities

• Own research and evaluation initiatives end-to-end, including problem framing and signal validation.
• Design ML-oriented data systems with optimized task definitions and annotation schemas.
• Analyze model and system failures to identify root causes and opportunities for improvement.
• Translate real-world behavior into structured evaluation frameworks and new data categories.
• Collaborate with researchers and domain experts to raise the signal bar continuously.
• Iterate on evaluations and feedback loops to enhance system performance.
• Act as a quality gate to ensure data integrity and signal strength.
• Partner with cross-functional teams to translate research progress into credible narratives.

Skills

Research signal quality

ML data systems design

Evaluation frameworks

Data annotation schemas

Communication

Ambiguity tolerance

Ownership & decisiveness

Cross-functional collaboration

Job description

Crossing Hurdles is seeking a Member of Technical Staff, Frontier AI, for remote collaboration. The role focuses on owning research and evaluation initiatives across problem framing, signal validation, and data-system design to drive robust AI capabilities.

You will design ML-oriented datasets and evaluation frameworks, translate system behavior into research opportunities, and work with researchers to raise the signal strength while owning end-to-end evaluation loops.