18 hours ago
Singapore, SingaporeMid Level
Base Salary
$100k - $170k/yr
Responsibilities
- 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 tools and methods to generate, mutate, validate, and improve synthetic tasks.
- Analyze agent performance to identify what tasks teach and where models fail.
- Develop metrics for task diversity, realism, learnability, and overall quality.
Requirements
- 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 synthetic data limitations.
- Experience designing or maintaining evaluation frameworks, benchmarks, or testing environments for AI agents or large language models.
- 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.
Benefits
- Visa sponsorship is available.
- On-site work in Singapore, Singapore.