Research Engineer, Synthetic Data

About the Role

Join an engineering team at an early-stage AI company building infrastructure for training and evaluating AI agents. You will develop synthetic data pipelines and methods that turn real-world professional workflows into useful training tasks, helping improve AI capabilities across technical and professional domains.

What You'll Do

• Build pipelines that generate realistic, structured, and challenging synthetic training tasks from domain-specific workflows.
• Collaborate with subject-matter experts to create tasks across professional and technical domains.
• Design methods and tools to generate, mutate, validate, and improve synthetic tasks.
• Analyze agent performance to understand what tasks teach and where models fail.
• Develop metrics for task diversity, realism, learnability, and overall quality.

What We're Looking For

• Two to four years of relevant experience in software engineering, machine learning engineering, or AI research.
• Hands-on experience applying synthetic data methods to build end-to-end data generation pipelines for AI or machine learning applications.
• Proficiency in Python, Linux, and containerization tools such as Docker.
• Experience with synthetic data quality criteria, evaluation metrics, and the limitations of synthetic data.
• Experience designing or maintaining evaluation frameworks, benchmarks, or testing environments for AI agents or large language models.
• A track record of independently delivering technical projects and building automated systems to generate, validate, or process structured data at scale.
• Strong attention to detail, first-principles reasoning, and clear communication for collaboration across time zones.
• Familiarity with reinforcement learning, agentic AI workflows, or LLM post-training is useful.

Compensation & Benefits

Compensation is USD 100,000 to USD 170,000 annually. Visa sponsorship is available.

Location

On-site in Singapore, Singapore.