Clera
ML Infrastructure Engineer
About this role
Join a seed-stage enterprise AI infrastructure company as an ML Infrastructure Engineer to design and deploy production ML systems that power a context layer for AI agents in regulated industries. You'll own end-to-end ML pipelines, fine-tune LLMs, build retrieval systems, and make architectural decisions alongside the founding team.
What you'll do
- Design and maintain production ML pipelines and systems for enterprise context layer
- Fine-tune and deploy LLMs and transformer-based models for regulated industry use cases
- Build information retrieval systems, knowledge graphs, and semantic understanding capabilities
- Architect large-scale data infrastructure optimized for distributed ML workloads
- Develop NLP solutions including text classification, entity extraction, and semantic understanding
- Own ML model evaluation, monitoring, and optimization in production environments
What they're looking for
- Machine Learning Engineering (5+ years production systems)
- LLM fine-tuning and transformer architectures
- Python and ML frameworks (PyTorch, TensorFlow)
- NLP (text classification, entity extraction, semantic understanding)
- Information retrieval and knowledge graph systems
- Large-scale data infrastructure and distributed systems
- Unsupervised learning and pattern discovery
- Prompt engineering and RAG techniques
Benefits
- Equity stake in early-stage company
- Competitive salary commensurate with experience
- Visa sponsorship available
- Remote work with on-site location in San Mateo, CA
- Work directly with founding team on architectural decisions
- Opportunity to shape technical foundation from ground up
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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
- Walk us through a production ML system you built end-to-end — what were the biggest challenges in taking it from research to production?
- Describe your experience fine-tuning LLMs. What framework did you use, and how did you handle data, training efficiency, and evaluation?