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Clera

Research Scientist / Research Engineer

San Francisco$100k–$300kfulltimemidAdded 3 days ago

About this role

Join a Y Combinator-backed startup building verifier-grounded training data for advanced AI systems. Work on methods to generate high-quality, formally validated data for LLMs, robotics, and scientific AI—directly shaping the company's technical direction from day one.

What you'll do

  • Design methods for generating verifiable training data grounded in formal systems, physics simulators, and executable tests
  • Research techniques to embed physics laws, biological facts, and logical consistency into natural language datasets
  • Build and maintain scalable data generation, validation, and quality assurance pipelines
  • Collaborate on research agendas for frontier AI models across LLMs, robotics, and scientific domains
  • Contribute to publications, benchmarks, and research artifacts advancing AI training data
  • Work directly with founders on technical strategy and execution

What they're looking for

  • Machine learning and deep learning
  • Natural language processing (NLP)
  • ML systems and data pipeline engineering
  • Formal methods or theorem proving
  • Physics simulation or scientific computing
  • LLM training, fine-tuning, or RLHF
  • Software engineering and research infrastructure
  • Robotics or AI for science

Benefits

  • Competitive salary ($100K–$300K annually)
  • Equity in early-stage, high-growth Y Combinator company
  • Opportunity to shape technical and research culture
  • Direct collaboration with founders and founding team
  • Work on frontier AI research with significant real-world impact
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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 a research project or system you built—what was the most challenging technical problem and how did you solve it?
  • How would you approach generating high-quality, verifiable training data for a domain where ground truth is difficult to define?
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