Clera
Research Engineer
About this role
Join an AI infrastructure company as a Research Engineer to build systems for training and evaluating frontier AI agents. You'll work on agent environments, benchmarks, and data pipelines, collaborating with a team of top researchers and founders on the technical foundation for next-generation AI.
What you'll do
- Design and iterate on agent training environments, running experiments to understand model behavior and failure modes
- Build systems for creating environments, improving data quality, and converting real-world workflows into tasks and benchmarks
- Develop internal tools to help teams produce higher-quality tasks, trajectories, and feedback mechanisms
- Manage the full lifecycle of agent training data from task design through trajectory collection and validation
- Partner with external vendors to identify pipeline bottlenecks and improve data quality and throughput
- Create metrics and analyses to assess whether tasks and evaluations effectively train frontier agents
What they're looking for
- Python
- Docker
- Linux
- Reinforcement learning benchmarks and evaluation
- Experimental design and metrics development
- AI agent training systems
- Data pipeline infrastructure
- Model diagnostics and analysis
Benefits
- Salary: $150,000–$250,000 USD annually
- Equity participation in Series A/B stage company
- Visa sponsorship available
- Work with world-class researchers and founders
- On-site presence in San Francisco with Singapore option
- Opportunity to shape frontier AI agent research
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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.
View all jobs at CleraLikely interview questions
- Describe your experience building or working with benchmarks for reinforcement learning or agent training—what made you assess them as effective or ineffective?
- Walk us through a time you designed and ran an experiment to diagnose a model's failure mode or data quality issue. What did you learn?