Research Engineer - Machine Learning (ML)

Eon collects large-scale neuroscientific data sets to train machine learning based brain emulations. We believe it is possible to scale this technology in a safe, secure and trustworthy manner in the next decade and empower humanity in unprecedented ways.

Role

Collaborating with a diverse team, including product managers, researchers, and engineering departments, your role involves conducting research on the application of cutting-edge of ML technologies to large-scale neuro datasets and transforming these insights into scalable, production-ready solutions.

Responsibilities

• Design, train, and fine-tune transformer-based ML models and systems, ensuring their applicability and effectiveness in neuroscience.
• Develop and maintain production-grade ML systems, ensuring their scalability, efficiency, and reliability.
• Implement benchmarks that evaluate quality, safety, security, and trustworthiness in ML models and systems developed.
• Work in tandem with cross-functional teams, including product development and data infrastructure
• Engage in collaborative research efforts to explore new ML architectures, including image and video transformer models and multimodal systems.
• Contribute to the creation of state-of-the-art (SOTA) foundation models for both invasive and non-invasive neuroscientific datasets.
  
  

Skills

• Demonstrated exceptional ability (3-5+ years) in ML engineering, particularly with PyTorch, including hands-on experience with training and fine-tuning transformer-based machine learning models.
• Demonstrated capability in developing production-level machine learning systems.
• Any of the following
• Experience with image and video transformer models.
• Expertise in training multimodal models and experimenting with novel architectures.
• Experience with applying machine learning techniques to neuroscientific datasets
• Previous work on scaling laws for modalities
  

We expect everybody, independent of their role to be

• Practicing proactive, concise, and clear written communication.
• Exceptionally output driven and a well-calibrated, fast, autonomous, and diligent problem-solver.
• Excited about startup athmosphere - high initiative, agile, and a can-do attitude in a fast changing environment.
  
  

Representative projects

These are examples of projects that you would be working on when joining us:

• Using gpt architectures to train a non-invasive brain activity foundation model based on public datasets
• Implement a modality agnostic ML training pipeline for neuroscientific datasets to train multimodal brain data models
• Create synthetic data sets based on ML models that helps to align various datasets or improve overall performance of models
  
  

Salary

Competitive salaries, including equity, apply.