Summary

Principal machine learning engineer to turn research into production-grade ml systems for a proactive ai smart assistant. You will own end-to-end ml pipelines (training, evaluation, inference, deployment) and build scalable, reliable inference with strong gpu optimization and deployment practices.

Responsibilities

• Build end-to-end ml pipelines for training and deployment
• Fine-tune and adapt models using lora and qlora
• Architect scalable inference balancing latency and reliability
• Design data systems for synthetic and real training data
• Implement evaluation pipelines for robustness and safety
• Optimize gpu performance for memory efficiency and latency
• Deploy and scale ml systems under production constraints