Founding Lead Machine Learning Engineer
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El puesto
Own end-to-end ML stack delivery: data pipelines, training, evaluation, and inference.
Architect and adapt frontier transformer models; design scalable, efficient inference systems.
Build robust data systems for high-quality synthetic and real-world training data, with alignment and guardrails.
Develop evaluation frameworks for performance, robustness, safety, and bias; optimize GPU deployment for latency and scale.
Shape early product direction, experiment with new use cases, and apply frontier techniques (retrieval-augmented training, mixture-of-experts, multimodal).
Operate with full ownership in a zero-to-one setting, collaborating with founders to define long-term architecture and capabilities.
Architect and adapt frontier transformer models; design scalable, efficient inference systems.
Build robust data systems for high-quality synthetic and real-world training data, with alignment and guardrails.
Develop evaluation frameworks for performance, robustness, safety, and bias; optimize GPU deployment for latency and scale.
Shape early product direction, experiment with new use cases, and apply frontier techniques (retrieval-augmented training, mixture-of-experts, multimodal).
Operate with full ownership in a zero-to-one setting, collaborating with founders to define long-term architecture and capabilities.
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