About this role
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.