About the Role Join a research and engineering team building synthetic data systems for AI agents. You will develop methods and infrastructure that turn real-world workflows into high-quality training tasks, helping expand and evaluate 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. • Design methods for generating diverse, realistic, and learnable tasks. • Develop tools to mutate, validate, and improve synthetic tasks. • Analyze model and agent performance to understand task effectiveness and identify failure modes. • Create metrics to assess task diversity, realism, learnability, and overall quality. What We're Looking For • Two or more years of experience in software engineering, machine learning engineering, or AI research, with a technical background. • Hands-on experience applying synthetic data methods to build end-to-end data generation pipelines for AI or machine learning. • Proficiency in Python, Linux, and containerization tools such as Docker. • Experience with evaluation frameworks, benchmarks, or testing environments for AI agents or large language models. • Understanding of synthetic data quality, evaluation metrics, and limitations, backed by research or production work. • Experience building automated systems to generate, validate, mutate, or process structured data at scale. • Ability to own technical projects from conception through delivery in loosely defined problem spaces. • Strong attention to detail, communication skills, and ability to reason about task design, scoring, and edge cases. • Familiarity with reinforcement learning, agent workflows, or LLM post-training is helpful. Compensation & Benefits Annual salary range: $100,000 to $170,000 USD. Visa sponsorship is available. Location On-site in Singapore, Singapore.