About this role
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
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Build pipelines that transform domain-specific workflows into realistic, structured, and challenging synthetic training tasks.
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Collaborate with subject-matter experts to create tasks across professional and technical domains.
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Design methods for generating diverse, realistic, and learnable tasks.
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Develop tools to mutate, validate, and improve synthetic tasks.
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Analyze model and agent performance to understand task effectiveness and identify failure modes.
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Create metrics to assess task diversity, realism, learnability, and overall quality.
What We're Looking For
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Two or more years of experience in software engineering, machine learning engineering, or AI research, with a technical background.
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Hands-on experience applying synthetic data methods to build end-to-end data generation pipelines for AI or machine learning.
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Proficiency in Python, Linux, and containerization tools such as Docker.
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Experience with evaluation frameworks, benchmarks, or testing environments for AI agents or large language models.
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Understanding of synthetic data quality, evaluation metrics, and limitations, backed by research or production work.
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Experience building automated systems to generate, validate, mutate, or process structured data at scale.
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Ability to own technical projects from conception through delivery in loosely defined problem spaces.
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Strong attention to detail, communication skills, and ability to reason about task design, scoring, and edge cases.
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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.