MLE, ML Platform

Job Description

Job Description

About zaimler

zaimler is building the semantic platform that links fragmented enterprise data and extracts meaning with knowledge-distilled models. We’re creating the foundation for AI systems that don’t just generate, but retrieve, link, and reason over enterprise knowledge.

In just over a year, we’ve begun partnering with Fortune 500 design partners in insurance, travel, and technology, deploying semantic AI infrastructure into some of the world’s most complex data ecosystems. Our platform enables enterprises to make data AI-ready from the start : automating ontology creation, data mapping, and retrieval-augmented reasoning at scale.

Our team comes from LinkedIn, Visa, Meta, and Branch, and has spent decades solving data and infrastructure challenges at scale. Backed by top VCs, we’re building the next foundational layer for enterprise AI.

About the Role

You’ll join our ML team focused on turning raw enterprise data into structured, contextualized knowledge graphs and embeddings. You’ll develop novel and highly scalable algorithms for ML and data engineering to make our overall system more efficient, experiment with new approaches for distilling large models into smaller, more efficient ones; improve retrieval, ranking, and reasoning performance through feedback loops; and prototype methods that help LLMs extract and act on real-world knowledge.

We're looking for someone who thrives on iteration, cares about building with rigor, and is hungry to learn from some of the best engineers and researchers in the field.

What You’ll Be Doing

• Develop new algorithms and data structures to improve extraction, tagging and resolution
• Collaborate closely with infra and data engineers to scale your research into production-ready components
• Prototype and refine models for extracting structured knowledge from text
• Apply knowledge distillation techniques to compress and optimize LLMs for downstream tasks
• Explore the use of reinforcement learning and feedback loops for improving model behavior
• Build evaluation pipelines for entity linking, retrieval, and semantic consistency
• Read, implement, and build upon recent research in LLM alignment, distillation, and symbolic grounding

Prior Experience

Nice to Have

Why Join

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.