About the Role
Join an AI evaluation and reinforcement learning team building synthetic data pipelines for AI agents. You will turn real-world professional workflows into scalable training tasks and develop ways to measure whether those tasks improve model capabilities.
What You'll Do
Build pipelines that transform domain-specific workflows into realistic, structured, and challenging synthetic training tasks.
Collaborate with subject-matter experts to create tasks across professional and technical domains.
Develop methods and tools to generate, mutate, validate, and improve synthetic tasks.
Analyze agent performance to understand what tasks teach and where they fail.
Create metrics for task diversity, realism, learnability, and overall quality.
What We're Looking For
Two or more years in software engineering, machine learning engineering, or AI research, with relevant experience building data pipelines or ML infrastructure.
Hands-on experience applying synthetic data research methods to build end-to-end generation pipelines.
Proficiency in Python and experience working in Linux environments with Docker.
Understanding of synthetic data quality, evaluation metrics, and the limitations of generated data.
Experience building evaluation frameworks, benchmarks, or testing environments for AI agents or language models.
Ability to independently deliver technical projects, build automated data systems, and spot subtle quality issues and edge cases.
Familiarity with reinforcement learning, agent workflows, or model post-training is useful.
Compensation & Benefits
Salary range: USD 100,000 to 170,000 annually. Visa sponsorship is available.
Location
On-site in Singapore, Singapore.