Founding Lead Machine Learning Engineer
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Die Stelle
Shape the core technical direction of the ML stack, including model selection, training strategy, and long-term architecture.
Build end-to-end training pipelines: data → training → eval → inference.
Design new model architectures or adapt frontier models and fine-tune using state-of-the-art methods (LoRA/QLoRA, SFT, DPO, distillation).
Architect scalable inference systems (vLLM, TensorRT-LLM, DeepSpeed) and develop data systems for high-quality synthetic and real-world data.
Develop alignment, safety, guardrails, and evaluation frameworks across performance, robustness, safety, and bias.
Own deployment—GPU optimization, latency reduction, scaling policies, and shape early product direction while exploring frontier techniques.
Build end-to-end training pipelines: data → training → eval → inference.
Design new model architectures or adapt frontier models and fine-tune using state-of-the-art methods (LoRA/QLoRA, SFT, DPO, distillation).
Architect scalable inference systems (vLLM, TensorRT-LLM, DeepSpeed) and develop data systems for high-quality synthetic and real-world data.
Develop alignment, safety, guardrails, and evaluation frameworks across performance, robustness, safety, and bias.
Own deployment—GPU optimization, latency reduction, scaling policies, and shape early product direction while exploring frontier techniques.
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