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.