Research Engineer, Synthetic Data

Sobre el puesto

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.

Publicada el 8 nov 2026