Machine Learning Engineer

Summary

Join Tether's Brain & AI team as a Machine Learning Engineer to develop AI models enhancing our understanding of neural mechanisms. This role involves building encoding and decoding models and applying this knowledge to real-world applications like brain-computer interfaces. You will collaborate with data scientists, improve data processing pipelines, maintain high code quality, and adapt algorithms for various computing environments. The position requires a degree in a quantitative field and 3+ years of relevant experience. Tether is at the forefront of integrating AI with brain-computer interface technologies, leveraging deep learning and generative models to decode brain activity. We are committed to innovation that is accessible, transparent, and privacy-focused.

Requirements

• Degree in Computer Science, Statistics, Informatics, Physics, Math, Neuroscience or another quantitative field
• 3+ years of experience of working in industry or research
• 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
• Strong scientific background and ability to formulate and test novel hypotheses with proper experiments, draw conclusions and support claims
• Proficient in managing unstructured datasets with strong analytical skills
• Demonstrated project management and organizational skills
• Proven ability to support and collaborate with cross-functional teams in a dynamic environment

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
• Write and revise papers, participate in conferences, communicate and disseminate results

Preferred Qualifications

• PhD and research experience in Computer Science, Statistics, Informatics, Physics, Math, Neuroscience or another quantitative field
• Scientific publications in top-tier AI and neuroscience conferences (NeurIPS, ICLR, ICML, AAAI, CVPR, Cosyne, SFN, CNN ecc) 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, ecc)