Machine Learning Engineer (Brain and AI): Tether

Tether, a pioneer in the global financial revolution and a leader in digital assets, is hiring a motivated and skilled Machine Learning Engineer to join its dynamic Brain & AI team. This role is at the forefront of integrating artificial intelligence with brain-computer interface (BCI) technologies, leveraging deep learning and generative models to decode and interpret brain activity.

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. This is an opportunity to join a global, remote-first team of top talent and contribute to revolutionary, privacy-focused innovation.

Key Responsibilities:
Develop and evaluate scalable deep learning algorithms that are central to the company's 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 to improve performance and reliability.

Adapt machine learning and neural network algorithms to optimize performance in various computing environments, including distributed clusters and GPUs.

Write and revise scientific papers, participate in conferences, and communicate research results.

Maintain high standards of code quality, organization, and automation across all projects.

Basic Qualifications:
A degree in Computer Science, Statistics, Informatics, Physics, Math, Neuroscience, or another quantitative field.

3+ years of experience working in industry or research.

Strong programming skills in Python, with experience developing machine learning algorithms or infrastructure using Python and PyTorch.

Experience in deep learning techniques such as supervised, semi-supervised, self-supervised learning, or generative modeling.

A strong scientific background with the ability to formulate and test novel hypotheses.

Preferred Qualifications:
A PhD and research experience in a relevant quantitative field.

A record of scientific publications in top-tier AI and neuroscience conferences (e.g., NeurIPS, ICLR, ICML, Cosyne).

Familiarity with deep learning libraries such as Hugging Face, Transformers, Accelerator, and Diffusers.

Hands-on experience in training and fine-tuning generative models like diffusion models or large language models.

Experience with non-invasive neural data (fMRI, EEG, MEG) or invasive neural recordings (ECoG, MEA).