Bridge Neurotech is building a non-invasive interface for the mind. Our aim is to image the brain's activity using ultrasound without surgery or other modifications to the body. First research study enrolling in Redwood City, California. Backed by General Catalyst.
About the role
As the Realtime Pipeline Lead, you will own one realtime path end to end: multi-Gbit/s RF data off an ultrasound probe through GPU beamforming, clutter filtering, and imaging preprocessing; ML preprocessing; neural decoder inference; and decoded output to a BCI application. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Expert modern C++ and experience with CUDA and GPU kernels, with pipelines you wrote or substantially rewrote for signal-processing, imaging, or perception workloads in production
- You have taken trained PyTorch (or equivalent) models to GPU deployment under hard latency budgets, using TensorRT, ONNX Runtime, Triton or hand-built CUDA inference
- You have built and owned production realtime or HPC systems other engineers depended on, at multi-Gbit/s or equivalent data rates, and you were the person accountable when latency targets were missed
- Working fluency with cloud infrastructure: object storage and storage tiers, compute instances with GPUs, IAM, and the sync between an on-site acquisition system and cloud storage
- Strong data structures, algorithms and systems fundamentals: memory layout, host-device transfer, pinned memory, streams, concurrency
- Enough Python and PyTorch to read a model, profile it, and have an informed opinion about architecture trade-offs that affect latency
More about this role
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.
As the Realtime Pipeline Lead, you will own one realtime path end to end: multi-Gbit/s RF data off an ultrasound probe through GPU beamforming, clutter filtering, and imaging preprocessing; ML preprocessing; neural decoder inference; and decoded output to a BCI application.
These components share GPU and memory, inside a latency budget tight enough for closed-loop decoding with deterministic tail latency. Today the pieces exist; the job is to make them one production system and to be accountable for it. As the owner of the realtime stack, you set its architecture, and you decide what to extend, replace, or push upstream (we build on open primitives like the Mach beamformer ).
You define the model-serving contract with the ML/AI Lead, the probe data handoff with the Embedded Systems Engineer, the imaging pipeline with the Imaging Scientist, and the decoded-output interface with the...
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