About The Position
The Research Engineer, Synthetic Data will build the synthetic data pipeline for an AI company developing infrastructure for AI training data. The role turns domain-specific professional and technical workflows into synthetic training tasks that are realistic enough to teach useful behavior, structured to generate at scale, and challenging enough to expand model capabilities. It is a hands-on research engineering position focused on designing how synthetic tasks are created, mutated, and validated, and analyzing what those tasks teach models and agents. It is not a commercial or privacy-systems role. The position is full-time and starts ASAP. The role can be performed on-site in San Francisco or Singapore, or remotely from Singapore or US Pacific time zones. Remote candidates should be able to overlap approximately 70–80% with San Francisco or Singapore business hours; the exact daily schedule will be agreed during hiring. The team is Engineering, and English is the stated language.
Requirements
- Proficiency in Python, Docker, and Linux environments.
- Experience with synthetic data research methods; applicants should be ready to describe this experience in their application.
- A strong understanding of what constitutes good synthetic data and its limitations.
- Experience building synthetic data pipelines end to end, including where there was no fully prescribed roadmap.
- Experience working on environments, evaluations, or benchmarks.
- Clear written and spoken English.
- Comfort collaborating remotely across time zones.
Nice To Haves
- Attention to detail, including the ability to catch subtle inconsistencies or edge cases in synthetic data.
- Ability to reason from first principles about task design, scoring, and failure modes.
- Comfort working in unstructured problem spaces or early-stage environments.
Responsibilities
- Work with subject-matter experts to create synthetic tasks for training AI agents across professional and technical domains.
- Design synthetic task-generation methods that produce diverse, realistic, and learnable tasks.
- Build systems and tooling to mutate, validate, and improve synthetic tasks.
- Analyze model and agent performance on synthetic tasks to understand what the tasks teach and where they fail.
- Develop metrics to quantify and understand synthetic task diversity, realism, learnability, and related qualities.
- Take synthetic data pipelines from early research through to reliable systems, including in areas without a fully prescribed roadmap.
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What This Job Offers
Job Type
Full-time
Education Level
No Education Listed
