Master Thesis focusing on developing machine learning models for lithium - ion cell sorting at Fraunhofer LBF. Involvement in innovative projects addressing circular economy in battery recycling.
About the role
- Engenheiro(a) de Machine Learning na Mundiale projetando e validando modelos de linguagem. Trabalhando com NLP e automação conversacional em um ambiente colaborativo e dinâmico.
Responsibilities
- Design, train, and validate language models (LLMs) for NLP applications.
- Build and maintain scalable pipelines for text pre- and post-processing.
- Develop and integrate services and APIs for model consumption in messaging systems and conversational automation.
Requirements
- Proven experience in systems development (with a focus on APIs and integrations with language models).
- Proficiency in Fine-Tuning, Transfer Learning, and Prompt Engineering techniques.
- Solid knowledge of applied NLP (tokenization, embeddings, RAG, etc.).
- Ability to work with distributed and scalable architectures.
- Experience with frameworks such as PyTorch, TensorFlow, or Hugging Face.
- Experience with MLOps and model deployment tools (Docker, FastAPI, MLflow).
- Interest in messaging, automation, and AI applied to conversational products.
Benefits
- Flexible Benefit (you choose how to use it according to your needs: culture, transportation app, subscriptions, gym, meals, etc.); 🎉🚗💼🏋️🍔
- Unimed Unipart Health Plan; 🏥
- Career path; 🚀
- Hybrid work; 🏡
- Day Off; 🌞
- Short Friday; 😎
- Collaborative and dynamic work environment. 🤝💼
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
No Education Requirement
Tech skills
Location requirements
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