Triton Inference Server Developer Jobs

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Bridge Neurotech, Inc. Redwood City, CA

Redwood City, CA

Today

Full time Cuda Data Pipeline Data Processing

Bridge Neurotech is seeking a software engineer to own a tightly coupled, end-to-end realtime pipeline for neural decoding.

Own the full realtime path: raw RF data ingestion, beamforming, processing (including clutter filtering), ML preprocessing, model inference, and decoded output
Build and mature the ML inference engine, including:
- Model loading from artifacts
- GPU memory residency management
- Batching strategy for latency-constrained inference
- TensorRT/ONNX runtime integration or hand-built CUDA inference
- Kernel-level integration with upstream imaging compute
Integrate, extend, and contribute to GPU beamforming where appropriate
Meet and maintain realtime throughput and latency goals at multi-Gbit/s ingest with deterministic tail latency suitable for closed-loop decoding
Define and own the model serving contract with the ML/AI Lead, including a path for model updates without interrupting service
Build the streaming and storage layer to support raw and processed data for both offline training and realtime inference
Collaborate to define upstream interfaces to the device acquisition layer and downstream interfaces to BCI applications
Implement observability across every stage and create benchmarks and regression tests to lock in pipeline performance and inference correctness as the system evolves

BS/MS/PhD in computer science, electrical engineering, or related field
8+ years building production realtime or HPC systems
Expert-level CUDA and modern C++, including experience writing or extensively modifying GPU kernels for signal processing or imaging workloads
Proven experience building production ML inference systems by deploying trained PyTorch (or equivalent) models to GPUs under hard latency budgets, including hands-on work with TensorRT, ONNX Runtime, Triton, or hand-built CUDA inference engines
Demonstrated realtime data pipeline experience at multi-Gbit/s rates with tight latency budgets
Strong grasp of data structures, algorithms, and signal processing for imaging or RF, including:
- Beamforming concepts
- FFTs
- IQ demodulation
- SVD-based filtering
- Autocorrelation
Proficiency in Python and the PyTorch ecosystem to collaborate with the ML team, identify production-impacting changes, and discuss model architecture trade-offs that affect latency

Experience with functional imaging or other high-data-rate imaging modalities
Experience integrating ML inference with upstream realtime compute, including shared GPU memory layouts and minimizing host-device round-trips
Experience with high-bandwidth I/O: GPUDirect Storage, RDMA, kernel-bypass networking, NVMe arrays

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