Research Engineer, Synthetic Data

ABOUT THE ROLE Join an engineering team at an early-stage AI company building infrastructure for training and evaluating AI agents. You will develop synthetic data pipelines and methods that turn real-world professional workflows into useful training tasks, helping improve AI capabilities across technical and professional domains. WHAT YOU'LL DO - Build pipelines that generate realistic, structured, and challenging synthetic training tasks from domain-specific workflows. - Collaborate with subject-matter experts to create tasks across professional and technical domains. - Design methods and tools to generate, mutate, validate, and improve synthetic tasks. - Analyze agent performance to understand what tasks teach and where models fail. - Develop metrics for task diversity, realism, learnability, and overall quality. WHAT WE'RE LOOKING FOR - Two to four years of relevant experience in software engineering, machine learning engineering, or AI research. - Hands-on experience applying synthetic data methods to build end-to-end data generation pipelines for AI or machine learning applications. - Proficiency in Python, Linux, and containerization tools such as Docker. - Experience with synthetic data quality criteria, evaluation metrics, and the limitations of synthetic data. - Experience designing or maintaining evaluation frameworks, benchmarks, or testing environments for AI agents or large language models. - A track record of independently delivering technical projects and building automated systems to generate, validate, or process structured data at scale. - Strong attention to detail, first-principles reasoning, and clear communication for collaboration across time zones. - Familiarity with reinforcement learning, agentic AI workflows, or LLM post-training is useful. COMPENSATION & BENEFITS Compensation is USD 100,000 to USD 170,000 annually. Visa sponsorship is available. LOCATION On-site in Singapore, Singapore.