Founding ML Engineer for Multimodal Mind Interfaces

NeuroTech X; San Francisco (CA); On-site; USD 180,000 - 280,000; Full time

Job summary

Voyage, a Maison for Mind Computing, seeks a Founding Machine Learning Engineer to shape the next generation of non-invasive neural interfaces. You will design architectures around new inductive biases, study representation learning on unlabeled data, and coordinate tightly with hardware and data collection at the edge of research and product.

We value engineers who can turn abstract data into robust models across modalities, align with experimental rigour under high uncertainty, and help define

Qualifications

• Strong deep learning fundamentals and ability to design architectures around new inductive biases.
• Sharp sense for representation learning and working with unlabeled data.
• Ability to reason about signal-to-noise situations and integrate multiple modalities.
• Experimental rigour and speed under high uncertainty in hardware/data collection.
• Experience building foundational models and continual learning is useful.

Responsibilities

• Design architectures around new inductive biases for multisensory data.
• Work with unlabeled data to improve representation learning.
• Coordinate with hardware and data collection for experiments.
• Build and test foundational models and tokenizers; manage continual learning.
• Prototype and validate models in a fast, iterative loop.

Skills

Deep learning fundamentals

Architectures for new inductive biases

Representation learning

Multimodal data handling

Experimental rigour

Job description

Voyage, a Maison for Mind Computing, seeks a Founding Machine Learning Engineer to shape the next generation of non-invasive neural interfaces. You will design architectures around new inductive biases, study representation learning on unlabeled data, and coordinate tightly with hardware and data collection at the edge of research and product.

We value engineers who can turn abstract data into robust models across modalities, align with experimental rigour under high uncertainty, and help define