Semantic ML Engineer for Enterprise Data & LLMs

zaimler; San Mateo (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

zaimler is seeking an ML/AI engineer to build systems that read messy enterprise data and turn it into a semantic model usable by agents. You will shape knowledge extraction, natural language understanding, retrieval, and evaluation to prove correctness in real customer domains.

You will work on the LLM layer, improve the retrieval stack, and ensure production-scale pipelines. Expect close collaboration with customers and cross-functional teams to ship impactful improvements.

Qualifications

• Two years of real production ML experience with enterprise data.
• Strong Python and PyTorch, ability to produce maintainable code.
• Familiarity with transformers, embeddings, and tokenization.
• Experience with LLMs in retrieval, fine-tuning, or evaluation.
• Comfort with customer-facing work and near-term impact.

Responsibilities

• Build and improve NLP and retrieval systems that extract knowledge from enterprise data.
• Work on the LLM layer: prompting, fine-tuning, RAG architectures.
• Enhance the retrieval stack: semantic search, vector storage, hybrid methods, reranking.
• Develop evaluation harnesses to measure quality and catch regressions.
• Maintain production data pipelines at scale and collaborate with customers.
• Collaborate with product and platform engineers to align solutions with business needs.

Skills

Python

PyTorch

Transformers

Embeddings

LLMs

Production ML

Vector storage

Ray

Spark

Kafka

vLLM

Tools

LoRA

QLoRA

Job description

zaimler is seeking an ML/AI engineer to build systems that read messy enterprise data and turn it into a semantic model usable by agents. You will shape knowledge extraction, natural language understanding, retrieval, and evaluation to prove correctness in real customer domains.

You will work on the LLM layer, improve the retrieval stack, and ensure production-scale pipelines. Expect close collaboration with customers and cross-functional teams to ship impactful improvements.