Founding Lead Machine Learning Engineer
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Die Stelle
Founding technical role shaping the core ML stack from model selection to long-term architecture and data strategy.
Build end-to-end training pipelines (data, training, eval, inference) and design scalable inference systems.
Design and adapt frontier models (e.g., LLaMA, Mistral, Qwen, Claude-compatible) and implement state-of-the-art fine-tuning (LoRA/QLoRA, SFT, DPO, distillation).
Own alignment, safety, and guardrail strategies; develop robust evaluation across performance, safety, and bias.
Own deployment: optimize GPUs, reduce latency, and scale systems; build data pipelines for high-quality synthetic and real-world data.
Shape early product direction, experiment with new use cases, and explore frontier techniques like retrieval-augmented training, Mixture-of-Experts, and multimodal architectures.
Build end-to-end training pipelines (data, training, eval, inference) and design scalable inference systems.
Design and adapt frontier models (e.g., LLaMA, Mistral, Qwen, Claude-compatible) and implement state-of-the-art fine-tuning (LoRA/QLoRA, SFT, DPO, distillation).
Own alignment, safety, and guardrail strategies; develop robust evaluation across performance, safety, and bias.
Own deployment: optimize GPUs, reduce latency, and scale systems; build data pipelines for high-quality synthetic and real-world data.
Shape early product direction, experiment with new use cases, and explore frontier techniques like retrieval-augmented training, Mixture-of-Experts, and multimodal architectures.
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