Clera
ML Infrastructure Engineer
About this role
Join an early-stage AI infrastructure startup as an ML Infrastructure Engineer to design and deploy production ML systems that power governed AI agents for regulated enterprises. You'll own core ML pipelines, work with LLMs and NLP at scale, and shape the technical direction alongside the founding team.
What you'll do
- Design and deploy end-to-end ML pipelines and production systems at scale
- Fine-tune LLMs and transformer architectures for enterprise AI features
- Build information retrieval systems, knowledge graphs, and ontology-based data models
- Apply NLP techniques including text classification, entity extraction, and semantic understanding
- Architect large-scale data infrastructure and distributed systems for ML workloads
- Monitor, evaluate, and optimize ML models in production environments
What they're looking for
- Python and ML frameworks (PyTorch or TensorFlow)
- LLM fine-tuning and transformer-based architectures
- NLP (text classification, entity extraction, semantic understanding)
- Information retrieval and knowledge graph systems
- Unsupervised learning and pattern discovery
- Large-scale data infrastructure and distributed systems
- Prompt engineering and RAG techniques
- Production ML system deployment and monitoring
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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've built end-to-end—what were the key architectural decisions and how did you handle scaling?
- Describe your experience fine-tuning LLMs. What frameworks did you use, and what challenges did you encounter around data preparation or evaluation?