{"id":3374297,"url":"https://alion.io/job/bridge-neurotech-ml-software-engineering-intern-phd","title":"ML Software Engineering Intern (PhD)","company":{"id":4445191,"name":"Bridge Neurotech","domain":"bridgeneurotech.com","url":"https://alion.io/company/bridge-neurotech","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":true,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"Backend","role_family":"Backend","seniority":"intern","employment_type":"internship","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Redwood City, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"JAX","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-10-12T23:26:43Z","employer_posted_date":"2026-10-12","last_verified_at":"2026-11-01T13:43:42Z","board_verified":true,"closed_at":null,"days_open":19,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":19},"description":"About Bridge Neurotech\nBridge 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.\nAbout the role\nWe are looking for a PhD student in machine learning or a related field to join Bridge for a six-month internship or co-op, working on the models and infrastructure behind our neural decoding pipeline: from raw functional ultrasound acquisition through image reconstruction, preprocessing, and decoding.\nRecent 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. This has been demonstrated with fMRI, in research settings. Our work is to bring these techniques to functional ultrasound: a modality with superior spatial and temporal resolution, portability, and a path to consumer deployment.\nYou will own a well-scoped project from open question to result, working closely with our ML/AI Engineering Lead.\nWhat you’ll do\nDesign, implement, train, and evaluate models for one part of the pipeline: fUSI reconstruction, signal preprocessing, or downstream decoding.\n\nRun the experiments: form and test hypotheses about why a model is not learning, design ablations, and tune architectures, losses, and training schedules.\n\nExtend models to new data regimes, for example 2D to 3D volumetric data, new probes, or new subjects, and build the preprocessing needed to train on them.\n\nContribute to our training and evaluation infrastructure: experiment tracking, metrics, and the regression checks that tell us when a model is actually better.\n\nDebug numerical and performance problems, for example NaNs under mixed precision, silent shape bugs, and GPU memory ceilings.\n\nWork directly with our imaging scientists and neuroscientists to understand where the data comes from and what its artifacts mean.\n\nReproduce results from the literature that matter to our roadmap, and present what you find to the team.\n\nWho you are\nRequired\nCurrently enrolled in a PhD program in machine learning, computer science, electrical engineering, computational neuroscience, physics, or a related field.\n\nAvailable full time for six months.\n\nStrong Python and hands-on PyTorch development (or an equivalent framework such as JAX or TensorFlow).\n\nExperience taking a model from a paper or a prototype to something that trains reliably on real data.\n\nComfort with large numerical datasets and memory-mapped arrays.\n\nSolid grounding in first principles, for example linear algebra and signal processing.\n\nGood software engineering practices: version control, testing, and code review.\n\nSelf-directed and self-motivated, with the ability to execute on a goal rather than a step-by-step guide.\n\nNice to have\nExperience with neural operators (FNO), implicit neural representations, or physics-informed machine learning architectures.\n\nExposure to neural decoding, brain-computer interfaces, or neuroimaging data.\n\nExperience with mixed-precision or multi-GPU training.\n\nPublications at machine learning or neuroscience venues, or contributions to open-source machine learning tooling.\n\nWhat we offer\nA six-month project with real ownership, scoped with you around our decoding roadmap.\n\nMentorship from our ML/AI Engineering Lead, and direct collaboration with leading clinicians and neuroscientists at top-tier academic institutions.\n\nWork on a modality early enough that the tooling does not exist yet. Much of what you build will be the first of its kind.\n\nBridge Neurotech is an equal opportunity employer. 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