ML/AI Engineer

Bridge Neurotech, Inc.; Redwood City (CA); On-site; USD 140,000 - 210,000; Full time

Benefits offered by this job

Excellent medical benefits

6% match 401(k)

Job summary

Bridge Neurotech, Inc. is seeking an ML/AI Engineer to design, scale, and deploy models behind its neural decoding pipeline, including fUSI reconstruction, signal preprocessing, and downstream decoding.

This onsite role focuses on building robust training infrastructure and optimizing performance for real-time applications. You will own end-to-end development from research to production, collaborate with imaging scientists, and push the frontier of neural decoding with foundation models.

Qualifications

• PhD in ML, CS, EE, computational neuroscience, physics, or related field; or a BS/MS with 2–3 years ML systems experience.
• Strong Python and PyTorch development experience.
• Track record of taking a model from paper to trained system on real data.
• End-to-end ownership of open-ended technical problems (thesis/publications/open-source).

Responsibilities

• Design, implement, train, and evaluate models across the pipeline including fUSI reconstruction and preprocessing.
• Drive an experimental agenda: test hypotheses, run ablations, tune architectures and training schedules.
• Extend models to new data regimes and subjects; build preprocessing pipelines for training.
• Own training infrastructure: experiment tracking, config management, checkpointing, mixed-precision, multi-GPU training, profiling.
• Define evaluation strategies with metrics and regression checks to confirm real improvements.
• Debug numerical and performance issues including NaNs under mixed precision and memory bottlenecks.
• Build inference paths for full-volume reconstruction and low-latency decoding.
• Collaborate with imaging scientists and neuroscientists to shape data collection.
• Track literature in neural operators and neuroimaging; reproduce key results and propose next steps.
• Review code/design and onboard new engineers.

Skills

Python

PyTorch

JAX

TensorFlow

ML systems

Linear algebra

Signal processing

End-to-end ownership

Software engineering

Education

PhD in ML/CS/EE/Neuroscience/Physics

BS/MS plus 2-3 years ML systems experience

Tools

Python

PyTorch

JAX

TensorFlow

Job description

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.