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.