Research Engineer, Synthetic Data

ABOUT THE ROLE Join a small engineering team building synthetic data systems for AI agents. You will turn domain-specific workflows into scalable training tasks, helping improve how models learn and perform in real-world applications. WHAT YOU'LL DO - Build pipelines that convert professional and technical workflows into realistic, structured training tasks. - Collaborate with subject-matter experts to develop tasks across distinct domains. - Design methods for generating diverse, realistic, and learnable synthetic tasks. - Create tools to mutate, validate, and improve generated tasks. - Analyze agent performance to understand task effectiveness and identify failure modes. - Develop metrics for 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, building and delivering data pipelines, ML infrastructure, or synthetic data systems. - Technical education or equivalent technical background, with strong Python skills and experience in Linux environments and Docker. - Hands-on experience applying synthetic data methods to build end-to-end generation pipelines for AI or machine learning. - Understanding of synthetic data quality, evaluation criteria, and limitations. - Experience building evaluation frameworks, benchmarks, or testing environments for AI agents or large language models. - Ability to independently deliver projects in open-ended settings, with careful attention to data quality and edge cases. - Familiarity with reinforcement learning, agent workflows, or LLM post-training is valuable. COMPENSATION & BENEFITS Annual base salary: $100,000 to $170,000 USD. Visa sponsorship is available. LOCATION On-site in Singapore, Singapore.