Research Engineer, Synthetic Data

About the Role

Join an engineering team at an early-stage AI company building infrastructure for training and evaluating AI agents. You will develop synthetic data methods and systems that turn real-world workflows into useful training tasks, helping expand agent capabilities across professional and technical domains.

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 generation methods and tools to mutate, validate, and improve synthetic tasks.
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Analyze model and agent performance to understand task strengths, gaps, and failure modes.
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Develop metrics for assessing task diversity, realism, learnability, and overall quality.
What We're Looking For
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Two to four years of relevant experience, with at least two years in software engineering, machine learning engineering, or AI research roles.
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Hands-on experience building end-to-end synthetic data generation pipelines for AI or machine learning applications.
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Proficiency in Python and experience working in Linux environments with Docker or similar containerization tools.
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Practical knowledge of synthetic data quality, evaluation metrics, and the limitations of generated data.
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Experience building automated systems for generating, validating, mutating, or processing structured datasets at scale.
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Background in AI agent or language model evaluation, benchmarks, testing environments, or post-training workflows.
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Ability to independently deliver technical projects, reason carefully about edge cases, and work effectively in an unstructured environment.
Compensation & Benefits
Annual salary: USD $150,000 to $250,000. Visa sponsorship is available.
Location
On-site in Singapore, Singapore.