Machine Learning Engineer Intern (100% Remote)

Join Tether and Shape the Future of Digital Finance

At Tether, we’re not just building products, we’re pioneering a global financial revolution. Our cutting‑edge solutions empower businesses—from exchanges and wallets to payment processors and ATMs—to seamlessly integrate reserve‑backed tokens across blockchains. By harnessing the power of blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, all at a fraction of the cost. Transparency is the bedrock of everything we do, ensuring trust in every transaction.

Innovate with Tether 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. Tether Power drives sustainable growth, optimizing excess power for Bitcoin mining with eco‑friendly practices in state‑of‑the‑art, geo‑diverse facilities. Tether Data fuels breakthroughs in AI and peer‑to‑peer technology, reducing infrastructure costs and enhancing global communications with cutting‑edge solutions like KEET, our flagship app redefining secure and private data sharing. Tether Education democratizes access to top‑tier digital learning, empowering individuals to thrive in the digital and gig economies. Tether Evolution pushes the boundaries of what is possible, crafting a future where innovation and human capabilities merge in powerful, unprecedented ways.

Why Join Us? Our team is a global talent powerhouse, working remotely from every corner of the world. If you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you.

About The Job

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. 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.

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 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.

Preferred Qualifications

• 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.).

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