At Tether, we are at the forefront of integrating artificial intelligence with brain-computer interface technologies. Our projects leverage deep learning, generative models, and representation learning to decode and interpret brain activity. With our foundation in both established and cutting-edge AI infrastructure, our mission is to bridge the gap between AI and neuroimaging, driving innovation that’s not only revolutionary but also accessible, transparent, and privacy-focused.
Job Description:
We are looking for a motivated and skilled machine learning engineer to join our dynamic Brain & AI team. This role focuses on developing AI models that enhance our understanding of neural mechanisms and apply this knowledge to real-world applications. You will be instrumental in pushing the boundaries of what’s possible in AI and neuroscience, helping to solve some of the most complex and fascinating challenges in the field today.
Responsibilities:
Develop and evaluate scalable deep learning algorithms that are central to our brain decoding initiatives.
Collaborate closely with data scientists to pioneer research in generative modeling and representation learning.
Identify bottlenecks in data processing pipelines and devise effective solutions, improving performance and reliability.
Maintain high standards of code quality, organization, and automatization across all projects.
Adapt machine learning and neural network algorithms to optimize performance in various computing environments, including distributed clusters and GPUs.
Basic Qualifications:
Strong programming skills in Python, with experience in developing machine learning algorithms or infrastructure using Python and PyTorch.
Experience in deep learning techniques such as supervised, semi-supervised, self-supervised learning, and/or generative modeling.
Proficient in managing unstructured datasets with strong analytic skills.
Demonstrated project management and organizational skills.
Proven ability to support and collaborate with cross-functional teams in a dynamic environment.
Preferred Qualifications:
Degree in Computer Science, Statistics, Informatics, Information Systems, or another quantitative field.
Familiarity with deep learning libraries such as Huggingface, Transformers, Accelerator and Diffuser.
Hands-on experience in training and fine-tuning generative models like diffusion models or large language models such as GPTs and LLAMAs.
Experience with data and model visualization tools.
