MLE, ML Platform

About zaimler

zaimler is building the next-generation 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 our semantic platform into some of the world’s most complex and high-volume data ecosystems. Our platform enables enterprises to make their 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:
• 2–4 years experience (research lab, internship, academic project, or early industry role) working in ML or NLP
• Strong fundamentals turning research papers, algorithms and mathematics into scalable and robust C++ code
• Solid understanding of ML fundamentals: training pipelines, loss functions, evaluation metrics
• Exposure to knowledge distillation, RLHF, or curriculum learning techniques
• Strong Python skills and familiarity with ML frameworks like PyTorch or TensorFlow
• Experience with language models and transformers (e.g., BERT, LLaMA, or similar)
• A collaborative mindset and willingness to work across research and engineering teams

Nice to Have:
• Familiarity with reinforcement learning, including policy optimization or reward modeling
• Familiarity with using and scaling algorithms to manipulate data, including graph algorithms, text-manipulation, embeddings.
• Experience with semantic representations such as knowledge graphs or entity embeddings
• Comfort working with tools like HuggingFace Transformers, Ray, or vLLM
• Understanding of small-model techniques (pruning, quantization, adapter layers)
• Interest in the LLM ecosystem and techniques for model alignment or prompt tuning
• Prior contributions to open-source projects or academic publications in ML/NLP

Why Join:
• A rare chance to be a founding engineer shaping both company and product direction.
• Competitive salary, benefits, and meaningful equity.
• Work alongside engineers and researchers from LinkedIn, Visa, Meta, and Branch.
• Onsite culture in San Mateo, designed for deep collaboration and high-velocity building.
• Full benefits package (Medical, Dental, Vision, 401k).
• We sponsor H-1B visas and assist with immigration processes.