Research Engineer, Synthetic Data

About the Role

This is a hands-on research engineering role focused on building synthetic data pipelines that turn real-world, domain-specific workflows into structured training tasks for AI agents. You will join a small, high-caliber engineering team of Olympiad medalists and published researchers, working at the core of a platform that powers reinforcement learning environments and post-training data for AI labs.

What You'll Do

• Design and build end-to-end synthetic data pipelines that convert domain-specific workflows into realistic, challenging training tasks.

• Collaborate with subject-matter experts to produce synthetic tasks for AI agents across professional and technical domains.

• Develop task generation methods that maximize diversity, realism, and learnability.

• Build tooling to mutate, validate, and iteratively improve synthetic tasks at scale.

• Analyze model and agent performance on synthetic tasks to identify what they teach and where they break down.

• Define and implement metrics to quantify synthetic task quality across diversity, realism, and learnability dimensions.

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.

• Proficiency in Python and hands-on experience with Docker and Linux environments.

• Demonstrated experience applying synthetic data research methods to build generation pipelines end-to-end.

• Strong understanding of synthetic data quality criteria, 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 with minimal predefined requirements or roadmap.

• Sharp eye for edge cases, subtle inconsistencies, and quality issues in synthetic or algorithmically generated datasets.

• Familiarity with reinforcement learning paradigms, agentic AI workflows, or LLM post-training pipelines is a plus.

• Strong communication skills for effective collaboration across time zones.

Compensation & Benefits

Salary range: $150,000 to $250,000 USD annually. Visa sponsorship is available.

Location

On-site in Singapore.