Research Engineer, Synthetic Data

About the Role

Join a small engineering team building synthetic data systems for AI agents. You will turn real-world professional workflows into scalable training tasks, helping improve how models learn and perform across practical domains.

What You'll Do

• Build pipelines that transform domain-specific workflows into realistic, structured synthetic training tasks.
• Work with subject-matter experts to create tasks for AI agents across professional and technical domains.
• Design generation methods that produce diverse, realistic, and learnable tasks.
• Develop tools to mutate, validate, and improve synthetic tasks.
• Analyze model and agent performance to understand task strengths, limitations, and failure modes.
• Create metrics for task diversity, realism, learnability, and overall quality.

What We're Looking For

• Two or more years of relevant experience in software engineering, machine learning engineering, or AI research, with a technical background.
• Hands-on experience applying synthetic data methods and delivering end-to-end data generation pipelines for AI or machine learning applications.
• Proficiency in Python and experience working in Linux environments with containerization tools such as Docker.
• Experience with synthetic data quality criteria, evaluation metrics, and the limitations of synthetic data.
• Background building or maintaining evaluation frameworks, benchmarks, or testing environments for AI agents or large language models.
• Experience automating the generation, validation, mutation, or processing of structured datasets at scale.
• Ability to independently deliver technical projects in loosely defined problem spaces, with care for edge cases and data quality.
• Familiarity with reinforcement learning, agent workflows, or LLM post-training is useful.

Compensation & Benefits

Base salary range: $100,000 to $170,000 USD annually. Visa sponsorship is available.

Location

On-site in Singapore, Singapore.