Research Engineer, Synthetic Data (Visa Sponsorship - Singapore)

Visa sponsorship available. A small engineering team is building synthetic data systems used to train AI agents. The work centers on converting domain-specific professional and technical workflows into scalable, realistic training tasks that help models learn and perform well in real-world applications. You will work closely with domain experts and shape how these tasks are generated, validated and measured.

What you'll do

• Build pipelines that turn professional and technical workflows into structured training tasks.
• Work with subject-matter experts to develop tasks across several distinct domains.
• Design methods for generating diverse, realistic and learnable synthetic tasks.
• Create tools to mutate, validate and improve generated tasks.
• Analyze agent performance to uncover task effectiveness and failure modes.
• Define metrics for task diversity, realism, learnability and overall quality.

What you need

• At least two years of experience in software engineering, machine learning engineering or AI research, including building data pipelines, ML infrastructure or synthetic data systems.
• Technical education or equivalent technical background, with strong Python skills and experience working in Linux environments and with Docker.
• Hands-on experience applying synthetic data methods to build end-to-end generation pipelines for AI or machine learning.
• Understanding of synthetic data quality, evaluation criteria and limitations.
• Experience building evaluation frameworks, benchmarks or test environments for AI agents or large language models.
• Ability to deliver projects independently in open-ended settings, with careful attention to data quality and edge cases.
• Familiarity with reinforcement learning, agent workflows or LLM post-training is a plus.

How to apply

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