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 methods and systems that turn real-world workflows into useful training tasks, helping expand agent capabilities across professional and technical domains. 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 generation methods and tools to mutate, validate, and improve synthetic tasks. Analyze model and agent performance to understand task strengths, gaps, and failure modes. Develop metrics for assessing task diversity, realism, learnability, and overall quality. What We're Looking For Two to four years of relevant experience, with at least two years in software engineering, machine learning engineering, or AI research roles. Hands-on experience building end-to-end synthetic data generation pipelines for AI or machine learning applications. Proficiency in Python and experience working in Linux environments with Docker or similar containerization tools. Practical knowledge of synthetic data quality, evaluation metrics, and the limitations of generated data. Experience building automated systems for generating, validating, mutating, or processing structured datasets at scale. Background in AI agent or language model evaluation, benchmarks, testing environments, or post-training workflows. Ability to independently deliver technical projects, reason carefully about edge cases, and work effectively in an unstructured environment. Compensation & Benefits Annual salary: USD $150,000 to $250,000. Visa sponsorship is available. Location On-site in Singapore, Singapore.