Research Engineer, Synthetic Data

ABOUT THE ROLE This is a Research Engineer role focused on building synthetic data pipelines for AI agent training, sitting within a ~15-person engineering team of Olympiad medalists and published researchers. You'll design generation methods, validation systems, and quality metrics that directly expand model capabilities — work that sits at the frontier of RL-based AI alignment. WHAT YOU'LL DO - Build end-to-end synthetic data pipelines that transform domain-specific workflows into structured, challenging training tasks for AI agents. - Collaborate with subject-matter experts to develop synthetic tasks spanning professional and technical domains. - Design task generation methods that produce diverse, realistic, and learnable training examples. - Build tooling to mutate, validate, and iteratively improve synthetic task quality. - Analyze model and agent performance on synthetic tasks to understand learning outcomes and failure modes. - Develop metrics to quantify synthetic task diversity, realism, learnability, and overall quality. WHAT WE'RE LOOKING FOR - 2–4 years of experience in software engineering, ML engineering, or AI research roles delivering data pipelines, ML infrastructure, or synthetic data systems. - Hands-on experience applying synthetic data research methods to build end-to-end data generation pipelines for AI/ML applications. - Proficiency in Python and experience developing in Linux environments using containerization tools such as Docker. - Demonstrated understanding of synthetic data quality criteria and evaluation metrics — diversity, realism, learnability — and their inherent limitations. - Experience designing, implementing, or maintaining evaluation frameworks, benchmarks, or testing environments for AI agents or large language models. - Experience building automated systems to generate, validate, mutate, or process structured datasets at scale. - Track record of independently owning and delivering technical projects end-to-end with minimal predefined requirements. - Ability to detect edge cases, inconsistencies, and quality issues in synthetic or algorithmically generated datasets. - Comfort operating in unstructured, early-stage environments and reasoning from first principles. - Strong communication skills for effective remote collaboration across time zones. - Nice to have: Familiarity with reinforcement learning paradigms, agentic AI workflows, or LLM post-training pipelines. COMPENSATION & BENEFITS Salary range: $150,000 – $250,000 USD annually. Visa sponsorship is available. LOCATION On-site in San Francisco, CA, United States. Singapore-based candidates are also considered.