Staff Applied Machine Learning Engineer

Job description

**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** This is a staff-level role for a seasoned AI/ML engineer who can operate at the intersection of research and production. You'll serve as the technical bridge between our research and applied engineering teams — translating cutting-edge ideas into robust, scalable systems while setting the strategic direction for how we train, evaluate, and deploy models. Deeply hands-on, but able to zoom out and set the agenda. We're looking for someone who has been here before. Built the infra, shaped the strategy, and knows what good looks like at every layer of the ML stack. ### What You Will be Doing - Bridge research and applied engineering, ensuring ideas move from concept to production with rigor and speed - Own training and evaluation infrastructure, including tuning, modeling pipelines, and evaluation frameworks - Build and evolve feature stores that serve both model development and production workloads - Optimize agentic workflows end-to-end for performance, reliability, and scale - Set technical strategy for how zaimler trains, evaluates, and deploys models as the platform grows - Partner closely with leadership to define the ML roadmap and make key architectural decisions ### Prior Experience - PhD preferred; Master's with exceptional experience considered - 8–10 years of experience in ML or AI engineering, with meaningful time in platform or lab environments - Track record of building and owning AI/ML infrastructure end-to-end, not just contributing to it - Deep experience with training pipelines, evaluation frameworks, and feature store design - Hands-on experience optimizing agentic or multi-step LLM workflows in production - Has operated as a lead or Head of AI at a startup or within a high-autonomy team - Strong strategic instincts; able to set direction, make tradeoffs, and communicate them clearly ### Nice to Have - Experience on both the AI infra and applied research sides of an organization - Familiarity with Knowledge Extraction, NLP, or semantic graph systems - Experience with GPU optimization, vLLM, Ray, or similar serving and training tools - Background working with or alongside research teams (lab, academic, or industry)