AI Technical Lead (Remote)
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Key skills for this role
About the Role
Hire Feed seeks an AI Technical Lead to improve frontier AI models through evaluation, failure analysis, and data system design. The role involves owning research initiatives, designing ML-oriented data systems, and collaborating with researchers.
Key Skills for This Role
Responsibilities
- Own research and evaluation initiatives end to end—problem framing, data design, quality calibration, and signal validation
- Design machine learning oriented data systems that include 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
- Iterate rapidly on evaluations, datasets, and feedback loops to improve model and system performance
Requirements
- Experience in research signal judgment for AI/ML systems, including evaluation design and validation of experimental results
- Strong background in ML oriented data design, with proficiency in defining annotation schemas and rubrics that align with model training objectives
- Ability to translate operations to research insights, bridging gaps between domain expertise and technical execution
- Hands on experience with reinforcement learning environments or related ML evaluation systems
- Background in designing and iterating on data pipelines that maintain quality while achieving velocity in production environments
Full Job Posting
Role Overview
- This role is a full time position focused on improving frontier AI models through rigorous evaluation, failure analysis, and iterative development. The work centers on designing and optimizing machine learning oriented data systems, translating real world problems into structured evaluation framewor
Key Responsibilities
- Own research and evaluation initiatives end to end—problem framing, data design, quality calibration, and signal validation.
- Design machine learning oriented data systems that include 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.
- Iterate rapidly on evaluations, datasets, and feedback loops to improve model and system performance.
Required Skills & Qualifications
- Experience in research signal judgment for AI/ML systems, including evaluation design and validation of experimental results.
- Strong background in ML oriented data design, with proficiency in defining annotation schemas and rubrics that align with model training objectives.
- Ability to translate operations to research insights, bridging gaps between domain expertise and technical execution.
- Hands on experience with reinforcement learning environments or related ML evaluation systems.
- Background in designing and iterating on data pipelines that maintain quality while achieving velocity in production environments.
More About the Opportunity
- This role offers the chance to contribute to the development of cutting edge AI systems used by labs and enterprises to train foundational models and build reliable AI agents.
- You’ll work within a robust data engine that includes an AI recruiter agent, a high performance data platform, and performance monitoring systems, enabling scalable impact across global expert networks.
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