Machine Learning Engineer Intern (100% Remote)

Join Tether and shape the future of digital finance. At Tether, we’re pioneering a global financial revolution with cutting-edge solutions that empower businesses to seamlessly integrate reserve-backed tokens across blockchains. Transparency is the bedrock of everything we do, ensuring trust in every transaction.

Our innovative product suite features the world’s most trusted stablecoin, USDT, relied upon by hundreds of millions worldwide, alongside pioneering digital asset tokenization services. We are at the forefront of integrating artificial intelligence with brain-computer interface technologies.

About The Job: We are looking for a motivated and skilled machine learning intern to join our dynamic Brain & AI team for 3 to 6 months. This role focuses on developing AI models that enhance our understanding of neural mechanisms, building encoding and decoding models, and applying this knowledge to real-world applications such as brain-computer interfaces.

• 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 automation across all projects.
• Adapt machine learning and neural network algorithms to optimize performance in various computing environments, including distributed clusters and GPUs.
• Write and revise papers, communicate and disseminate results.

• Hold a degree and currently be involved in a PhD program in Computer Science, Statistics, Informatics, Physics, Math, Neuroscience, or another quantitative field.
• Have 1+ years of experience working in industry or research.
• Possess programming skills in Python, with experience in developing machine learning algorithms or infrastructure using Python and PyTorch.
• Have experience in deep learning techniques such as supervised, semi-supervised, self-supervised learning, and/or generative modeling.
• Have a scientific background and the ability to formulate and test novel hypotheses with proper experiments, draw conclusions, and support claims.
• Manage unstructured datasets with strong analytical skills.
• Possess project management and organizational skills.
• Prove ability to support and collaborate with cross-functional teams in a dynamic environment.

• Research experience in Computer Science, Statistics, Informatics, Physics, Math, Neuroscience, or another quantitative field.
• Scientific publications in AI and neuroscience conferences or peer-reviewed journals.
• Familiarity with deep learning libraries such as PyTorch, 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.
• Experience with non-invasive neural data (fMRI, EEG, MEG) or invasive neural recordings (ECoG, MEA, etc.).