Machine Learning Engineer Intern

Responsibilities

• Develop and evaluate scalable deep learning algorithms for brain decoding initiatives
• Collaborate with data scientists to pioneer research in generative modeling and representation learning
• Identify and resolve bottlenecks in data processing pipelines to improve performance and reliability
• Maintain high standards of code quality, organization, and automation across all projects
• Optimize machine learning and neural network algorithms for distributed clusters and GPUs
• Write and revise scientific papers to communicate and disseminate research results

Requirements

• Currently enrolled in a PhD program in Computer Science, Statistics, Physics, Math, Neuroscience, or a related quantitative field
• 1+ years of experience in industry or research settings
• Proficiency in Python and experience developing machine learning algorithms using PyTorch
• Experience with deep learning techniques including supervised, semi-supervised, or self-supervised learning
• Strong scientific background with the ability to formulate hypotheses and conduct rigorous experiments
• Ability to manage unstructured datasets and apply strong analytical skills

Preferred Qualifications

• Research experience or scientific publications in AI and neuroscience conferences or journals
• Familiarity with libraries such as Huggingface, Transformers, Accelerator, and Diffuser
• Hands‑on experience training and fine‑tuning generative models (e.g., Diffusion models, LLMs like GPT or Llama)
• Experience with data and model visualization tools
• Experience working with neural data, including non‑invasive (fMRI, EEG, MEG) or invasive (ECoG, MEA) recordings