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
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Build pipelines that convert professional and technical workflows into realistic, structured training tasks.
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Collaborate with subject-matter experts to develop tasks across distinct domains.
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Design methods for generating diverse, realistic, and learnable synthetic tasks.
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Create tools to mutate, validate, and improve generated tasks.
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Analyze agent performance to understand task effectiveness and identify failure modes.
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Develop metrics for 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, building and delivering data pipelines, ML infrastructure, or synthetic data systems.
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Technical education or equivalent technical background, with strong Python skills and experience in Linux environments and Docker.
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Hands-on experience applying synthetic data methods to build end-to-end generation pipelines for AI or machine learning.
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Understanding of synthetic data quality, evaluation criteria, and limitations.
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Experience building evaluation frameworks, benchmarks, or testing environments for AI agents or large language models.
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Ability to independently deliver projects in open-ended settings, with careful attention to data quality and edge cases.
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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.
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