Research Engineer, Synthetic Data

ABOUT THE ROLE Join a small engineering team building synthetic data systems for AI agents. You will turn real-world professional workflows into scalable training tasks, helping improve how models learn and perform across practical domains. WHAT YOU'LL DO - Build pipelines that transform domain-specific workflows into realistic, structured synthetic training tasks. - Work with subject-matter experts to create tasks for AI agents across professional and technical domains. - Design generation methods that produce diverse, realistic, and learnable tasks. - Develop tools to mutate, validate, and improve synthetic tasks. - Analyze model and agent performance to understand task strengths, limitations, and failure modes. - Create metrics for task diversity, realism, learnability, and overall quality.

WHAT WE'RE LOOKING FOR - Two or more years of relevant experience in software engineering, machine learning engineering, or AI research, with a technical background. - Hands-on experience applying synthetic data methods and delivering end-to-end data generation pipelines for AI or machine learning applications. - Proficiency in Python and experience working in Linux environments with containerization tools such as Docker. - Experience with synthetic data quality criteria, evaluation metrics, and the limitations of synthetic data. - Background building or maintaining evaluation frameworks, benchmarks, or testing environments for AI agents or large language models. - Experience automating the generation, validation, mutation, or processing of structured datasets at scale. - Ability to independently deliver technical projects in loosely defined problem spaces, with care for edge cases and data quality. - Familiarity with reinforcement learning, agent workflows, or LLM post-training is useful.

COMPENSATION & BENEFITS Base salary range: $100,000 to $170,000 USD annually. Visa sponsorship is available. LOCATION On-site in Singapore, Singapore.