Founding Lead Machine Learning Engineer
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Die Stelle
Lead the end-to-end development of scalable ML systems, from data pipelines to production-grade inference.
Design and experiment with model architectures and frontier open-source models, applying methods like LoRA/QLoRA, SFT, DPO, and distillation.
Build robust training pipelines and data systems for synthetic and real-world training data.
Own deployment, GPU optimization, latency reduction, and scaling policies for a global consumer AI product.
Develop alignment, safety guardrails, and evaluation frameworks covering performance, robustness, safety, and bias.
Shape early product direction, explore frontier techniques (retrieval-augmented training, mixture-of-experts, multimodal models), and ship AI-powered experiences from zero.
Design and experiment with model architectures and frontier open-source models, applying methods like LoRA/QLoRA, SFT, DPO, and distillation.
Build robust training pipelines and data systems for synthetic and real-world training data.
Own deployment, GPU optimization, latency reduction, and scaling policies for a global consumer AI product.
Develop alignment, safety guardrails, and evaluation frameworks covering performance, robustness, safety, and bias.
Shape early product direction, explore frontier techniques (retrieval-augmented training, mixture-of-experts, multimodal models), and ship AI-powered experiences from zero.
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