Research Engineer, Synthetic Data

Навыки

• Data Quality
• Docker
• Linux
• LLM
• Machine Learning
• Ответственность за результат
• Python

О компании и продукте

• 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.

Задачи

• Build and maintain the end-to-end synthetic data pipeline, converting domain-specific workflows into realistic, structured, and challenging training tasks for AI agents
• Collaborate with subject-matter experts to generate synthetic tasks across professional and technical domains
• Design synthetic task generation methods that produce diverse, realistic, and learnable outputs
• Build tooling to mutate, validate, and iteratively improve synthetic tasks at scale
• Analyze model and agent performance on synthetic tasks to understand what they teach and where they break down
• Develop metrics to quantify synthetic task diversity, realism, learnability, and overall quality

Требования

• 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
• Hands-on experience applying synthetic data research methods to build end-to-end data generation pipelines for AI/ML applications
• Proficiency in Python
• comfortable working in Linux environments with containerization tools such as Docker
• Demonstrated understanding of synthetic data quality criteria and evaluation metrics (diversity, realism, learnability) and their limitations — from production or research work
• 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
• Proven ability to independently own and deliver technical projects end-to-end with minimal predefined requirements

Будет плюсом

• Experience detecting edge cases, inconsistencies, or quality issues in synthetic or algorithmically generated datasets
• Experience creating synthetic tasks, data, or evaluations across multiple distinct professional or technical domains
• Familiarity with reinforcement learning training paradigms, agentic AI workflows, or LLM post-training pipelines
• You'll thrive here if you
• Reason from first principles about task design, scoring, and failure modes
• Are detail-oriented and naturally spot subtle inconsistencies in data and systems
• Are energised by early-stage, ambiguous environments rather than frustrated by them
• Communicate clearly and collaborate effectively across time zones

Условия

• Salary: $150,000 – $250,000 USD annually
• Visa sponsorship available
• 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

Паспорт вакансии

История публикации

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