Founding Lead Machine Learning Engineer
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El puesto
Own the full ML product lifecycle: data collection & curation, training, evaluation, and deployment.
Design and extend model architectures, including adapting frontier open-source models for real-world use.
Implement state-of-the-art fine-tuning and alignment methods (LoRA/QLoRA, SFT, DPO, distillation).
Architect scalable inference systems using vLLM, TensorRT-LLM, and DeepSpeed with GPU-focused optimization.
Build robust data systems for high-quality synthetic and real-world training data; develop evaluation frameworks covering performance, robustness, safety, and bias.
Shape early product direction, explore frontier techniques (retrieval-augmented training, mixture-of-experts, multimodal models), and own end-to-end delivery.
Design and extend model architectures, including adapting frontier open-source models for real-world use.
Implement state-of-the-art fine-tuning and alignment methods (LoRA/QLoRA, SFT, DPO, distillation).
Architect scalable inference systems using vLLM, TensorRT-LLM, and DeepSpeed with GPU-focused optimization.
Build robust data systems for high-quality synthetic and real-world training data; develop evaluation frameworks covering performance, robustness, safety, and bias.
Shape early product direction, explore frontier techniques (retrieval-augmented training, mixture-of-experts, multimodal models), and own end-to-end delivery.
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