Research Engineer, Synthetic Data (Mid-Level)

About the Role

Join an AI evaluation and reinforcement learning team building synthetic data pipelines for AI agents. You will turn real-world professional workflows into scalable training tasks and develop ways to measure whether those tasks improve model capabilities.

What You'll Do

• Build pipelines that transform domain-specific workflows into realistic, structured, and challenging synthetic training tasks.

• Collaborate with subject-matter experts to create tasks across professional and technical domains.

• Develop methods and tools to generate, mutate, validate, and improve synthetic tasks.

• Analyze agent performance to understand what tasks teach and where they fail.

• Create metrics for task diversity, realism, learnability, and overall quality.

What We're Looking For

• Two or more years in software engineering, machine learning engineering, or AI research, with relevant experience building data pipelines or ML infrastructure.

• Hands-on experience applying synthetic data research methods to build end-to-end generation pipelines.

• Proficiency in Python and experience working in Linux environments with Docker.

• Understanding of synthetic data quality, evaluation metrics, and the limitations of generated data.

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

• Ability to independently deliver technical projects, build automated data systems, and spot subtle quality issues and edge cases.

• Familiarity with reinforcement learning, agent workflows, or model post-training is useful.

Compensation & Benefits

Salary range: USD 100,000 to 170,000 annually. Visa sponsorship is available.

Location

On-site in Singapore, Singapore.