About the Role
We're a ~15-person engineering team — made up of Olympiad medalists and published researchers — building infrastructure that aligns AI to real-world workflows through reinforcement learning environments and post-training data. We're hiring Research Engineers to own the synthetic data pipeline: transforming domain-specific workflows into scalable, high-quality training tasks for AI agents.
This is a high-ownership, low-bureaucracy role. You'll be working in genuinely unstructured problem spaces where the roadmap is yours to define. Visa sponsorship is available.
What You'll Do
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Build and maintain the end-to-end synthetic data pipeline, converting domain-specific workflows into realistic, structured, and challenging training tasks for AI agents.
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Collaborate with subject-matter experts to generate synthetic tasks across professional and technical domains.
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Design synthetic task generation methods that produce diverse, realistic, and learnable outputs.
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Build tooling to mutate, validate, and iteratively improve synthetic tasks at scale.
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Analyze model and agent performance on synthetic tasks to understand what they teach and where they break down.
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Develop metrics to quantify synthetic task diversity, realism, learnability, and overall quality.
What We're Looking For
Required:
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2–4 years of experience in software engineering, ML engineering, or AI research — with a track record of shipping data pipelines, ML infrastructure, or synthetic data systems.
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Hands-on experience applying synthetic data research methods to build end-to-end data generation pipelines for AI/ML applications.
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Proficiency in Python; comfortable working in Linux environments with containerization tools such as Docker.
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Demonstrated understanding of synthetic data quality criteria and evaluation metrics (diversity, realism, learnability) and their limitations — from production or research work.
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Experience designing, implementing, or maintaining evaluation frameworks, benchmarks, or testing environments for AI agents or large language models.
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Experience building automated systems to generate, validate, mutate, or process structured datasets at scale.
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Proven ability to independently own and deliver technical projects end-to-end with minimal predefined requirements.
Nice to Have:
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Experience detecting edge cases, inconsistencies, or quality issues in synthetic or algorithmically generated datasets.
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Experience creating synthetic tasks, data, or evaluations across multiple distinct professional or technical domains.
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Familiarity with reinforcement learning training paradigms, agentic AI workflows, or LLM post-training pipelines.
You'll thrive here if you:
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Reason from first principles about task design, scoring, and failure modes.
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Are detail-oriented and naturally spot subtle inconsistencies in data and systems.
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Are energised by early-stage, ambiguous environments rather than frustrated by them.
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Communicate clearly and collaborate effectively across time zones.
Compensation & Benefits
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Salary: $150,000 – $250,000 USD annually
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Visa sponsorship available
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Equity participation (early-stage startup)
Location
This role is on-site in San Francisco, CA. Candidates based in or willing to relocate to San Francisco are strongly preferred. The team also has a presence in Singapore.
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