About the Role

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

What You'll Do

Build pipelines that convert professional and technical workflows into realistic, structured training tasks.

Collaborate with subject-matter experts to develop tasks across distinct domains.

Design methods for generating diverse, realistic, and learnable synthetic tasks.

Create tools to mutate, validate, and improve generated tasks.

Analyze agent performance to understand task effectiveness and identify failure modes.

Develop metrics for task diversity, realism, learnability, and overall quality.

What We're Looking For

Two or more years of experience in software engineering, machine learning engineering, or AI research, building and delivering data pipelines, ML infrastructure, or synthetic data systems.

Technical education or equivalent technical background, with strong Python skills and experience in Linux environments and Docker.

Hands-on experience applying synthetic data methods to build end-to-end generation pipelines for AI or machine learning.

Understanding of synthetic data quality, evaluation criteria, and limitations.

Experience building evaluation frameworks, benchmarks, or testing environments for AI agents or large language models.

Ability to independently deliver projects in open-ended settings, with careful attention to data quality and edge cases.

Familiarity with reinforcement learning, agent workflows, or LLM post-training is valuable.

Compensation & Benefits

Annual base salary: $100,000 to $170,000 USD. Visa sponsorship is available.

Location

On-site in Singapore, Singapore.