Clera
Research Engineer
About this role
Join an early-stage AI infrastructure startup as a Research Engineer to build systems for training and evaluating frontier AI agents. You'll work across benchmarking, synthetic data, and quality automation, bridging research and engineering to directly shape how agents learn and improve.
What you'll do
- Design and build systems for creating environments, improving data quality, and converting real-world workflows into benchmarks and tasks
- Run experiments to understand agent failure modes, model behavior, and data quality issues
- Develop internal tools for researchers and data vendors to produce higher-quality tasks, trajectories, and feedback loops
- Manage the full lifecycle of agent training data from task design through trajectory collection, evaluation, and validation
- Collaborate with external vendors to identify bottlenecks and enhance data engine quality and throughput
- Create metrics and analyses to evaluate whether tasks, environments, and evaluations effectively train frontier agents
What they're looking for
- Python programming
- Docker containerization
- Linux administration
- Benchmark and evaluation design for RL training
- Experiment design and metrics development
- Research infrastructure and pipeline development
- Data quality assessment and validation
- Model behavior analysis
Benefits
- Competitive salary $150k–$250k annually
- Visa sponsorship available
- On-site role in San Francisco with remote flexibility for Singapore-based candidates
- High-impact work directly influencing platform direction
- Opportunity to work on frontier AI agent technology
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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
- Can you walk us through a benchmark or evaluation system you built—how did you ensure tasks were realistic and valuable for training?
- Describe your experience working with RL training pipelines. What data quality issues have you encountered and how did you address them?