Machine Learning Engineer

Overview

Accountabilities

• 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.

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.

Benefits

• Fully remote work environment with global collaboration opportunities.
• Work on cutting-edge AI and neuroscience technologies with real-world impact.
• Exposure to advanced research initiatives in deep learning, generative AI, and brain-computer interfaces.
• Opportunity to collaborate with leading experts in AI, fintech, and decentralized technologies.
• Fast-paced, innovation-focused culture with significant autonomy and ownership.
• Career growth opportunities within a rapidly evolving international organization.
• Flexible and globally distributed work environment that values transparency, innovation, and continuous learning.

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