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 design and build the pipelines that turn domain-specific workflows into scalable, high-quality training tasks for AI agents, directly expanding what the models can do. 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 understand what they teach and where they fail. - Develop metrics to quantify 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 a focus on data pipelines, ML infrastructure, or synthetic data systems. - Hands-on experience applying synthetic data research methods to build end-to-end data generation pipelines for AI/ML applications. - Proficiency in Python and comfortable working in Linux environments with containerization tools such as Docker. - Strong understanding of synthetic data quality criteria, including diversity, realism, and learnability, and awareness of its inherent limitations. - Experience designing, implementing, or maintaining evaluation frameworks, benchmarks, or testing environments for AI agents or large language models. - Proven ability to independently own and deliver technical projects end-to-end with minimal predefined requirements. - Detail-oriented approach to spotting edge cases and subtle inconsistencies in algorithmically generated datasets. - Familiarity with reinforcement learning training paradigms, agentic AI workflows, or LLM post-training pipelines is a plus. - Experience creating synthetic tasks or evaluations across multiple distinct professional or technical domains is a plus. - Comfortable thriving in unstructured, early-stage startup environments and collaborating across time zones. COMPENSATION & BENEFITS Salary range: $150,000 to $250,000 USD annually. Visa sponsorship is available. LOCATION On-site in Singapore.