Member of the Technical Staff - ML/AI Engineer

Summary: The ML/AI Engineer will own the neural decoding pipeline for functional ultrasound imaging, from raw data acquisition to image reconstruction and decoding. This role involves setting technical direction, extending models to new data regimes, and building robust training infrastructure while collaborating with imagin...

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 experienced ML/AI Engineer to own the models and infrastructure behind our neural decoding pipeline, from raw functional ultrasound acquisition through image reconstruction, preprocessing, and decoding. You will set technical direction for your area, make relevant calls on architecture and experimental priorities, and be responsible for results that move the company’s roadmap.

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, a path to clinical utility and, eventually, consumer deployment.

We are looking for someone with the seniority and experience to take ambiguous, high-stakes problems and turn them into shipped systems and trusted to make decisions with incomplete information. The scope is unusually wide for a role at any stage. You will report to our ML/AI Engineering Lead and work directly with our founding team, imaging scientists, and clinical collaborators.

What you’ll do

• Own major components of the pipeline end to end: fUSI reconstruction, signal preprocessing, and downstream decoding. Define what good looks like, build it, and be accountable for the results.
• Set the research and engineering agenda for your area: identify the highest-leverage open questions, form and test hypotheses, design ablations, and translate findings into decisions the team acts on.
• Extend models to new data regimes, including new dimensionalities (2D to 3D volumetric), new probes, and new subjects, and build the 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, with an eye toward eventual deployment.
• Work directly with our imaging scientists and clinical 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.
• Raise the engineering bar: lead code and design reviews, mentor engineers and interns as new team members onboard, and help establish the practices and culture of a growing technical team.
• Communicate clearly with technical and non-technical stakeholders about progress, risks, and tradeoffs.

Who you are

Required

• 5+ years of experience building, training, and shipping machine learning systems, or a PhD in machine learning, computer science, electrical engineering, computational neuroscience, physics, or a related field plus 3+ years of applied experience.
• Deep, hands-on expertise in Python and PyTorch (or an equivalent framework such as JAX), including the ability to take a model from a paper or prototype to something that trains reliably on real data.
• A track record of leading an open-ended technical problem from ambiguous question to shipped result, with evidence such as a deployed system, first authored-publications in top machine learning or scientific journals, or significant open-source work.
• Comfort with large numerical datasets, for example hundreds of gigabytes to multiple terabytes, and memory-mapped arrays.
• Strong grounding in first principles, including linear algebra, optimization, and signal processing, and the ability to reason about why a model is or is not learning.
• Experience owning training infrastructure or large-scale experimentation: distributed and multi-GPU training, mixed precision, memory profiling, gradient checkpointing, and diagnosing throughput bottlenecks.
• Excellent software engineering judgment: version control, testing, code review, and the discipline to build systems other people can depend on.
• Demonstrated ability to mentor junior engineers or interns and raise the quality of a team’s work.
• Strong written and verbal communication, including the ability to explain technical decisions and tradeoffs to a mixed audience.
• Comfortable with ambiguity and high autonomy. You set your own goals, prioritize ruthlessly, and execute without 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.
• Experience with foundation-model or representation-learning pipelines, including mapping signals into pretrained latent spaces.
• 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.