ML Infrastructure Engineer

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 at scale. Imagine 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, LinkedIn), who built one of the largest knowledge graphs in production in the industry at LinkedIn. We're growing and deploying with major enterprises across insurance, travel, and technology. If you want to build infrastructure that the next decade of enterprise AI runs on, we'd love to talk.

About the Role You'll own our inference and model-serving infrastructure end to end. This isn't a research role. It's a build role: you're setting up and scaling the systems that let our agents actually run in production, fast and reliably, at increasing concurrency. You report to Sofus and work closely with our ML and infra teams.

Set up and scale inference/Ray Serve for ML and LLM model serving, integrated with our data analysis and agent workflows Scale agent GPU infrastructure for concurrency and efficiency across multiple agent workloads Optimize and improve the engine builder and model server that power scalable agent orchestration

Proven ability to build scalable ML/AI platforms from scratch, end-to-end, for production use cases. You've owned a zero-to-one build before, or can show you're capable of it Deep understanding of the inference stack: vLLM, KV cache, and the optimization layers underneath model serving Experience building distributed systems for AI/ML workloads at scale, connecting them to real product or vertical integrations 3+ years of relevant experience. We care about capability, not tenure

Ray / Ray Serve experience Familiarity with AIBrix

A rare chance to shape both company and product direction as an early team engineer Work alongside engineers and researchers from LinkedIn, Visa, Meta, and Branch Onsite culture in San Mateo, built for deep collaboration and high-velocity building Full benefits (medical, dental, vision, 401k) We sponsor H-1B visas and assist with immigration

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