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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.\nWe'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.\nWhat You Will be Doing\nBridge research and applied engineering, ensuring ideas move from concept to production with rigor and speed\nOwn training and evaluation infrastructure, including tuning, modeling pipelines, and evaluation frameworks\nBuild and evolve feature stores that serve both model development and production workloads\nOptimize agentic workflows end-to-end for performance, reliability, and scale\nSet technical strategy for how zaimler trains, evaluates, and deploys models as the platform grows\nPartner closely with leadership to define the ML roadmap and make key architectural decisions\nPrior Experience\nPhD preferred; Master's with exceptional experience considered\n8-10 years of experience in ML or AI engineering, with meaningful time in platform or lab environments\nTrack record of building and owning AI/ML infrastructure end-to-end, not just contributing to it\nDeep experience with training pipelines, evaluation frameworks, and feature store design\nHands-on experience optimizing agentic or multi-step LLM workflows in production\nHas operated as a lead or Head of AI at a startup or within a high-autonomy team\nStrong strategic instincts; 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