Machine Learning Engineer
New
SpainFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 3+ years
- Required Skills
- PythonData AnalysisPyTorchDeep LearningGenerative AI
Requirements
- Bachelor’s degree in Computer Science, Mathematics, Physics, Statistics, Neuroscience, Informatics, or another quantitative discipline.
- 3+ years of professional or research experience in machine learning, deep learning, or AI engineering.
- Strong programming skills in Python and hands-on experience with machine learning frameworks such as PyTorch.
- Solid understanding of supervised, self-supervised, semi-supervised, and generative deep learning techniques.
- Proven ability to design experiments, evaluate models scientifically, and derive data-driven conclusions.
- Experience handling large or unstructured datasets with strong analytical and problem-solving capabilities.
- Excellent collaboration, communication, and organizational skills in cross-functional and remote environments.
- Familiarity with tools and libraries such as Hugging Face, Transformers, Diffusers, or Accelerator is highly valued.
- Experience working with generative models, large language models, or neural data such as EEG, fMRI, or MEG is considered a strong advantage.
- PhD-level research experience and publications in leading AI or neuroscience conferences are a plus.
Responsibilities
- Develop, train, and optimize scalable deep learning models for brain decoding and AI-driven neuroscience applications.
- Collaborate closely with research scientists and cross-functional teams to advance generative modeling and representation learning initiatives.
- Improve and maintain machine learning pipelines, ensuring high performance, scalability, and reliability across distributed environments and GPU systems.
- Contribute to research activities, including experimentation, hypothesis testing, technical documentation, and participation in conferences or scientific publications.
- Maintain high standards of software engineering, automation, code quality, and reproducibility across projects.
- Identify technical bottlenecks and implement innovative solutions to improve workflows, infrastructure, and model performance.
- Support the deployment and adaptation of AI models across diverse computing environments and evolving business needs.
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