🛠 Tech Stack

💼 About This Role

You'll develop deep learning models for brain-computer interface technologies, working at the intersection of AI, neuroscience, and digital finance. You'll build scalable machine learning infrastructure and contribute to groundbreaking research projects. This role offers strong exposure to research-oriented work with global collaboration in a fast-paced remote environment.

🎯 What You'll Do

• Develop scalable deep learning models for brain decoding and AI-driven neuroscience applications.
• Collaborate with research scientists on generative modeling and representation learning initiatives.
• Improve ML pipelines for high performance, scalability, and reliability on distributed GPU systems.
• Conduct experiments, document technical findings, and support research activities and publications.

📋 Requirements

• Bachelor's degree in Computer Science or a quantitative discipline.
• 3+ years of professional or research experience in machine learning, deep learning, or AI engineering.
• Strong programming skills in Python with hands-on experience in PyTorch.
• Solid understanding of supervised, self-supervised, semi-supervised, and generative deep learning techniques.

✨ Nice to Have

• Experience with generative models, large language models, or neural data (EEG, fMRI, MEG).
• Familiarity with Hugging Face, Transformers, Diffusers, or Accelerator.
• PhD-level research experience and publications in AI or neuroscience conferences.

🎁 Benefits & Perks

• 🌍 Fully remote work environment with global collaboration opportunities.
• 🧠 Cutting-edge AI and neuroscience technologies with real-world impact.
• 📚 Exposure to advanced research in deep learning, generative AI, and brain-computer interfaces.
• 🤝 Collaborate with leading experts in AI, fintech, and decentralized technologies.
• 🚀 Fast-paced innovation culture with significant autonomy and ownership.

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Call· 30 min
2. 2Technical Interview· 60 min
3. 3Take-home Assignment· 2 hours