Research Engineer, Synthetic Data

About the Role

Join a research and engineering team building synthetic data systems for AI agents. You will develop methods and infrastructure that turn real-world workflows into high-quality training tasks, helping expand and evaluate 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.

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

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

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

• Create metrics to assess 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, with a technical background.

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

• Proficiency in Python, Linux, and containerization tools such as Docker.

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

• Understanding of synthetic data quality, evaluation metrics, and limitations, backed by research or production work.

• Experience building automated systems to generate, validate, mutate, or process structured data at scale.

• Ability to own technical projects from conception through delivery in loosely defined problem spaces.

• Strong attention to detail, communication skills, and ability to reason about task design, scoring, and edge cases.

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

Compensation & Benefits

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

Location

On-site in Singapore, Singapore.