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.

Key responsibilities

• 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.

Required profile

• 2 to 4 years of experience in software engineering, machine learning engineering, or AI research, focusing 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, and their 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.

Required skills

• Python
• Docker
• Linux

What we offer

• Salary range $150,000–$250,000 USD annually.
• Visa sponsorship available.
• On‑site work location in Singapore.