Research Engineer, Synthetic Data (Mid-Level)

Job description

### 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.