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.

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.