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Clera

Research Engineer, Synthetic Data

San Francisco$150k–$250kfulltimemidAdded today

About this role

Research Engineer role focused on designing and building synthetic data pipelines to train AI agents, working within a high-performing team of researchers. You'll develop generation methods, validation systems, and quality metrics that advance model capabilities in RL-based AI alignment.

What you'll do

  • Build end-to-end synthetic data pipelines transforming domain workflows into structured training tasks for AI agents
  • Collaborate with subject-matter experts to develop synthetic tasks across professional and technical domains
  • Design task generation methods producing 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 identify learning outcomes and failure modes
  • Develop metrics quantifying synthetic task diversity, realism, learnability, and overall quality

What they're looking for

  • Python programming
  • Data pipeline design and implementation
  • Synthetic data generation and validation
  • Linux and containerization (Docker)
  • ML infrastructure and evaluation frameworks
  • Reinforcement learning and agentic AI workflows
  • Benchmark design for AI agents or LLMs
  • Large-scale structured dataset processing

Benefits

  • Competitive salary $150,000–$250,000 annually
  • Visa sponsorship available
  • Remote collaboration across time zones supported
  • On-site in San Francisco with Singapore option
  • Work at frontier of AI alignment research
  • Team of Olympiad medalists and published researchers
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Clera

Clera builds an agentic operating system that automates complex workflows and processes through AI agents, with a platform designed to simplify distributed infrastructure management for developers. The company is hiring Founding Engineers, Customer Engineers, and Product Engineers to develop both backend systems and user-facing interfaces across their AI automation products.

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Likely interview questions

  • Walk us through an end-to-end synthetic data pipeline you've built—what was the generation strategy, and how did you validate quality?
  • How do you approach designing synthetic tasks that are both realistic and learnable for AI agents?
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