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.
