Bridge Neurotech is building a non-invasive functional ultrasound imaging (fUSI) platform with AI-based neural decoding for brain-computer interface applications. It is a seed-stage neurotechnology company based in Redwood City, with custom hardware, imaging science, real-time compute, neural decoding and research studies under one roof.

About the role

This is a six-month internship or co-op for a PhD student in machine learning or a related field, working on the models and infrastructure behind the neural decoding pipeline: from raw functional ultrasound acquisition through image reconstruction, preprocessing and decoding. You will own a well-scoped project from open question to result, working closely with the ML/AI Engineering Lead.

What you will do

• Design, implement, train and evaluate models for one part of the pipeline: fUSI reconstruction, signal preprocessing or downstream decoding.
• Run the experiments: form and test hypotheses about why a model is not learning, design ablations, and tune architectures, losses and training schedules.
• Extend models to new data regimes such as 2D to 3D volumetric data, new probes or new subjects.
• Contribute to the training and evaluation infrastructure, including experiment tracking, metrics and regression checks.
• Debug numerical and performance problems such as NaNs under mixed precision and GPU memory ceilings.
• Reproduce results from the literature that matter to the roadmap and present findings to the team.

What they are looking for

• Currently enrolled in a PhD programme in machine learning, computer science, electrical engineering, computational neuroscience or physics.
• Available full time for six months.
• Strong Python and hands-on PyTorch, JAX or TensorFlow development.
• Experience taking a model from paper or prototype to something that trains reliably on real data.
• Comfort with large numerical datasets and memory-mapped arrays, and solid grounding in linear algebra and signal processing.