ML/AI Engineer

About Bridge Neurotech

Bridge Neurotech is building a non-invasive functional ultrasound imaging (fUSI) platform with AI-based neural decoding for brain-computer interface applications. We are a seed-stage neurotechnology company developing technology that has the potential to transform how we interact with the human brain.

About the role

We are looking for an ML/AI Engineer to own and scale key parts of the models and infrastructure behind our neural decoding pipeline, from raw functional ultrasound acquisition through image reconstruction, preprocessing, and decoding.

This role sits at the frontier of two rapidly converging fields: neural decoding and foundation models. Recent work has shown that brain activity can be mapped into the latent spaces of pretrained deep learning models to reconstruct visual perception and continuous language with remarkable fidelity. This has been demonstrated with fMRI, in research settings. Your job is to bring these techniques to functional ultrasound: a modality with superior spatial and temporal resolution, portability, and a path to consumer deployment.

This is a hands-on role with an unusual amount of surface area. You will scope and lead projects from open question to result with a high degree of independence, and help set the technical direction of our decoding work. You will report to our ML/AI Engineering Lead.

What you’ll do

• Design, implement, train, and evaluate models across the pipeline: fUSI reconstruction, signal preprocessing, and downstream decoding, and own the results for the components you lead.

• Drive the experimental agenda for your area: form and test hypotheses about why a model is not learning, design ablations, and tune architectures, losses, and training schedules, then turn what you find into decisions the team can act on.

• Extend models to new data regimes, for example new dimensionalities (2D to 3D volumetric), new probes, and new subjects, and build the preprocessing pipelines needed to normalize that data for training.

• Own the training infrastructure: experiment tracking, config management, checkpointing, mixed-precision and multi-GPU training, and profiling, built well enough for the rest of the team to rely on.

• Define the evaluation strategy: metrics, pre-launch tests, and regression checks that tell us when a model is actually better.

• Debug numerical and performance problems, for example NaNs under mixed precision, silent shape bugs, page-cache thrashing, dead gradients, and GPU memory ceilings.

• Build inference paths for full-volume reconstruction and low-latency real-time decoding.

• Work directly with our imaging scientists and neuroscientists to understand where the data comes from and what its artifacts mean, and help shape how future data is collected.

• Track the relevant literature in neural operators, neuroimaging, and neural decoding, reproduce the results that matter to our roadmap, and propose where we should go next.

• Review code and designs, and help onboard new engineers and interns as the team grows.

Who you are

Required

• A PhD in machine learning, computer science, electrical engineering, computational neuroscience, physics, or a related field; or a bachelor’s or master’s degree in one of those fields plus 2 to 3 years of experience building and shipping machine learning systems in a research or product setting.

• Strong Python and hands-on PyTorch development (or an equivalent framework such as JAX or TensorFlow).

• A track record of independently taking a model from a paper or a prototype to something that trains reliably on real data.

• Evidence of owning an open-ended technical problem end to end, such as a thesis, publications, open-source work, or a shipped system.

• Comfort with large numerical datasets, for example hundreds of gigabytes to multiple terabytes, and memory-mapped arrays.

• Solid grounding in first principles, for example linear algebra and signal processing.

• GPU proficiency: mixed precision, memory profiling, gradient checkpointing, and the ability to diagnose bottlenecks.

• Strong software engineering practices: version control, testing, code review, and coding conventions.

• Self-directed and self-motivated, with the ability to execute on a goal rather than a step-by-step guide.

Nice to have

• Experience with neural operators (FNO), implicit neural representations, or physics-informed machine learning architectures.

• Exposure to neural decoding, brain-computer interfaces, or neuroimaging data.

• Familiarity with complex-valued neural networks or spectral methods.

• Experience developing real-time or low-latency inference systems.

• Distributed or multi-GPU training at scale.

• Publications at machine learning or neuroscience venues, or contributions to open-source machine learning tooling.

• Prior work in a startup or small team, or other evidence of thriving without much scaffolding.

What we offer

• Real ownership of core technical infrastructure at a seed-stage company, with a direct path to growing into a senior or lead role as the team scales.

• Mentorship from our ML/AI Engineering Lead, and direct collaboration with leading clinicians and neuroscientists at top-tier academic institutions.

• Work on a modality early enough that the tooling does not exist yet. Much of what you build will be the first of its kind.

• Excellent medical benefits and a 6% match 401(k).

Bridge Neurotech is an equal opportunity employer. We encourage candidates of all backgrounds to apply.