ML/AI Engineer

Bridge Neurotech, Inc. is hiring an ML/AI Engineer (onsite) to build and scale the models and infrastructure behind its neural decoding pipeline.

Responsibilities

• Design, implement, train, and evaluate models across the pipeline, including fUSI reconstruction, signal preprocessing, and downstream decoding; own results for the components you lead
• Drive an experimental agenda for your area: form and test hypotheses when a model is not learning, run ablations, and tune architectures, losses, and training schedules
• Extend models to new data regimes such as new dimensionalities (for example 2D to 3D volumetric), new probes, and new subjects; build preprocessing pipelines to normalize data for training
• Own training infrastructure: experiment tracking, config management, checkpointing, mixed-precision, multi-GPU training, and profiling, built so others can rely on it
• Define evaluation strategy with metrics, pre-launch tests, and regression checks to confirm real model improvements
• Debug numerical and performance issues including 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
• Collaborate with imaging scientists and neuroscientists to understand data sources and artifacts, and help shape how future data is collected
• Track relevant literature in neural operators, neuroimaging, and neural decoding; reproduce key results for the roadmap and propose next steps
• Review code and designs; help onboard new engineers and interns as the team grows

Requirements

• 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)
• Track record of independently taking a model from paper/prototype to something that trains reliably on real data
• Evidence of end-to-end ownership of an open-ended technical problem (thesis, publications, open-source work, or shipped system)
• Comfort with large numerical datasets (hundreds of gigabytes to multiple terabytes) and memory-mapped arrays
• Solid grounding in first principles such as linear algebra and signal processing
• GPU proficiency: mixed precision, memory profiling, gradient checkpointing, and diagnosing bottlenecks
• Strong software engineering practices including version control, testing, code review, and coding conventions
• Self-directed and self-motivated; able to execute on a goal without step-by-step guidance

Technologies

• Python
• PyTorch
• JAX
• TensorFlow

Benefits

• Excellent medical benefits
• 6% match 401(k)

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 evidence of thriving without much scaffolding

Role focus: Own and scale key parts of the models and infrastructure behind a neural decoding pipeline from functional ultrasound acquisition through image reconstruction, preprocessing, and decoding; work at the frontier of neural decoding and foundation models.