Research Engineer, Synthetic Data (Pacific Time)

Overview

Research engineering seat building the synthetic data pipeline: turning domain-specific workflows into synthetic training tasks for AI agents, with the tasks' diversity, realism, learnability and failure modes analysed.

What you'll do

• Work with subject-matter experts to create synthetic tasks for training AI agents across a range of 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.
• Analyse model and agent performance on synthetic tasks to understand what the tasks are teaching 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 where the roadmap is not fully prescribed.

What you'll need

• Proficiency in Python, Docker and Linux environments.
• Experience with synthetic data research methods.
• A strong understanding of what "good synthetic data" means, and of its limitations.
• Experience building synthetic data pipelines end to end, including where there was no fully prescribed roadmap.
• Experience working on environments, evals or benchmarks.
• Clear written and spoken English, and comfort collaborating remotely across time zones.

Nice to have

• Detail-oriented enough to catch subtle inconsistencies or edge cases in synthetic data.
• Able to reason from first principles about task design, scoring and failure modes.
• Comfortable in unstructured problem spaces or early-stage environments.

Details

• Location: San Francisco or Singapore (on-site), or remote (Singapore or US Pacific time zones).
• Full-time role, starting ASAP. Language: English.
• Remote candidates' working hours should overlap about 70–80% with either San Francisco (US Pacific) or Singapore business hours; the exact daily schedule will be agreed during hiring.