Research Engineer, Synthetic Data

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.