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.