MLE, ML Platform

About zaimler

AI agents can't reason over data they don't understand. Enterprise data today is fragmented across dozens of systems with no shared context, meaning, or structure, and that's why most enterprise AI is failing. The shift from copilots to autonomous agents is creating an entirely new infrastructure layer, and we're building it.

zaimler is the context infrastructure for the agentic era: a platform that automatically discovers domain knowledge, maps relationships, and gives AI agents the semantic understanding to operate with precision gine knowledge graphs that support real-time inference, built for systems that need to reason, not just retrieve.

zaimler was founded by Biswajit Das (ex-VP Engineering, Truera), a Data Infra veteran and former Chief Architect at Visa, and Sofus Macskassy (ex-Director of Engineering, Linked In), who built one of the largest knowledge graphs in production in the industry 're a small, senior team at the seed stage, deploying with major enterprises across insurance, travel, and technology. If you want to build infrastructure that the next decade of AI runs on, we'd love to talk.

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 Tensor Flow
• 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 Hugging Face 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 Linked In, 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.

We value builders over résumés. If this role excites you but you don't check every box, we still want to hear from you. zaimler is an equal opportunity employer.

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.