Staff Applied Machine Learning Engineer at zaimler
Join zaimler.ai as a Staff Applied MLE to bridge the gap between research and production. You will work directly with the Chief Scientist to extract knowledge from heterogeneous enterprise data and build semantic graphs for real-time inference. This role is pivotal in developing the proprietary domain intelligence that prevents enterprise AI failure.
About the role
Build the missing infrastructure layer between fragmented data and AI agents, solving the "context gap" that causes most enterprise AI pilots to fail.
Work alongside elite leadership, including a CEO who was Chief Architect at Visa and a Chief Scientist who built LinkedIn’s massive production knowledge graph.
Impact Fortune 500 customers from day one, shipping high-stakes models that enable autonomous agents to reason over complex, proprietary enterprise data with precision.
Responsibilities
- Develop and adapt models for sophisticated knowledge extraction from diverse enterprise data sources to construct and improve high-scale AI Data Graphs.
- Fine-tune large and small language models with domain-specific context to ensure performance and reliability in complex, multi-step enterprise agentic workflows.
- Partner with infrastructure teams to build and evolve training pipelines, evaluation frameworks, and feature stores that support real-time inference and scale.
Requirements
- Holds a PhD or Master’s in Computer Science or ML with 4-10 years of experience deploying NLP or LLM models in production environments.
- Possesses deep technical expertise in at least one core area: knowledge extraction, semantic graph systems, embedding-based retrieval, or LLM fine-tuning and optimization.
- Thrives in high-autonomy startup environments and has a proven track record of building and owning end-to-end AI/ML infrastructure rather than just contributing components.