Research Engineer, Synthetic Data

ABOUT THE ROLE This is a Research Engineer role focused on synthetic data, sitting within a roughly 15-person engineering team of Olympiad medalists and published researchers. You will build the pipelines that turn domain-specific workflows into scalable, high-quality training tasks for AI agents, directly shaping what models learn and how well they perform. WHAT YOU'LL DO - Build end-to-end synthetic data pipelines that transform domain-specific workflows into realistic, structured, and challenging training tasks. - Collaborate with subject-matter experts to create synthetic tasks for AI agents across professional and technical domains. - Design task generation methods that produce diverse, realistic, and learnable outputs at scale. - Build tooling to mutate, validate, and continuously improve synthetic tasks. - Analyze model and agent performance on synthetic tasks to identify what they teach and where they break down. - Develop metrics to quantify synthetic task diversity, realism, learnability, and overall quality. WHAT WE'RE LOOKING FOR - 2 to 4 years of experience in software engineering, machine learning engineering, or AI research, with hands-on work building data pipelines, ML infrastructure, or synthetic data systems. - Proficiency in Python and experience developing in Linux environments using containerization tools such as Docker. - Demonstrated experience applying synthetic data research methods to build end-to-end data generation pipelines for AI/ML applications. - Strong understanding of synthetic data quality criteria and evaluation metrics, including diversity, realism, and learnability, as well as their inherent limitations. - Experience designing, implementing, or maintaining evaluation frameworks, benchmarks, or testing environments for AI agents or large language models. - Track record of independently owning and delivering technical projects end-to-end with minimal predefined requirements. - Experience building automated systems to generate, validate, mutate, or process structured datasets at scale. - Sharp eye for edge cases, inconsistencies, and quality issues in synthetic or algorithmically generated data. - Familiarity with reinforcement learning training paradigms, agentic AI workflows, or LLM post-training pipelines is a plus. - Comfortable operating in unstructured, early-stage environments and reasoning from first principles. - Strong communication skills for asynchronous, cross-timezone collaboration. COMPENSATION & BENEFITS Salary range: $150,000 to $250,000 USD annually. Visa sponsorship is available. LOCATION On-site in Singapore.