Senior Machine Learning Engineer
Kol
Sobre nós O varejo físico responde por mais de 80% das vendas no Brasil — e nenhuma das conversas entre vendedor e cliente virava dado. A KOL resolve isso: capturamos interações no PDV via áudio e entregamos inteligência real para gestores de varejo. Produto validado, pipeline ativo, estágio inicial. Cada pessoa que entra agora está construindo o que a KOL vai se tornar. Buscamos um Senior Machine Learning Engineer para compor o nosso Founding Team e assumir a camada de speech e inteligência conversacional — os sistemas que transformam conversas reais em lojas físicas em transcrições, insights estruturados e sinais acionáveis para gestores. Você vai trabalhar onde a qualidade da IA determina o valor que o cliente percebe. O que você fará – Assumir ownership pelo pipeline de speech-to-text e análise conversacional. – Avaliar e melhorar qualidade de transcrição em ambientes ruidosos de lojas físicas. – Trabalhar em diarização, identificação de papéis dos interlocutores e processamento de transcrições. – Desenhar fluxos com LLMs para extração estruturada de insights, resumos, objeções e comportamentos de venda. – Construir datasets de avaliação, métricas de qualidade, feedback loops e versionamento de modelos e prompts. – Fazer benchmarks de provedores de speech-to-text, diarização e LLMs. – Trabalhar com produto e engenharia para tornar insights de IA confiáveis e acionáveis. O que buscamos – Forte experiência em IA aplicada, engenharia de ML, NLP, speech ou engenharia de dados. – Forte experiência com Python. – Experiência construindo pipelines em produção, não apenas notebooks ou experimentos. – Experiência com LLMs, extração estruturada, versionamento de prompts e modelos, e avaliação. – Capacidade de trabalhar com dados reais, ambíguos e ruidosos. – Forte senso de produto em torno de qualidade, confiabilidade e confiança do usuário em sistemas de IA. Diferenciais – Experiência com speech-to-text, diarização, processamento de áudio ou dados conversacionais ruidosos. – Background em conversation intelligence, sales coaching ou analytics para call center. – Experiência com fluxos de anotação, revisão humana ou otimização de custo e latência em IA. Você pode ser a pessoa certa se... Você já colocou um pipeline de IA em produção, viu ele falhar de formas inesperadas com dados reais e construiu os mecanismos de avaliação para entender exatamente onde e por quê. Você não considera um modelo bom porque performou bem no benchmark: só quando funciona com os dados bagunçados do mundo real. Você se importa com a confiança do usuário final no output da IA — e sabe que essa confiança é construída com métricas, feedback loop e muito trabalho de avaliação, não com hype.
Vaga publicada há 6 dias atrás
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