Clera
Singapore
Research Engineer, AI Agent Evaluation
- AI
- Docker
- Linux
- Machine Learning
AI Summary
Build synthetic data systems that turn real-world professional workflows into scalable training tasks for AI agents. Develop pipelines, generation methods, tools, metrics, and evaluations for synthetic task quality and agent performance. Requires 2+ years in software engineering, machine learning engineering, or AI research, with Python, Linux, and Docker experience.
Key Highlights
Build synthetic data pipelines and tools for AI agent training tasks
Work with subject-matter experts across professional and technical domains
Create metrics for task diversity, realism, learnability, and quality
Requires 2+ years in software engineering, ML engineering, or AI research
Python, Linux, and Docker experience required
Key Responsibilities
Build pipelines that transform domain-specific workflows into realistic, structured synthetic training tasks
Work with subject-matter experts to create tasks for AI agents across professional and technical domains
Design generation methods that produce diverse, realistic, and learnable tasks
Develop tools to mutate, validate, and improve synthetic tasks
Analyze model and agent performance to understand task strengths, limitations, and failure modes
Create metrics for task diversity, realism, learnability, and overall quality
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Technical Skills Required
Benefits & Perks
Visa sponsorship
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Nice to Have
Familiarity with reinforcement learning, agent workflows, or LLM post-training
Job Description
About the Role
Join a small engineering team building synthetic data systems for AI agents. You will turn real-world professional workflows into scalable training tasks, helping improve how models learn and perform across practical domains.
What You'll Do
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