Triton Inference Server Engineer Jobs

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Today

Software Engineer - Realtime Pipeline

Bridge Neurotech, Inc. Redwood City, CA

Redwood City, CA

Today

Full time Cuda Data Pipeline Data Processing

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Job Description

Bridge Neurotech is seeking a Software Engineer to own and scale a real-time software pipeline for neural decoding workflows, from raw RF ingestion through ML inference and decoded outputs.

Responsibilities

Own the end-to-end real-time path: raw RF data ingestion, beamforming, processing (including clutter filtering), ML preprocessing, model inference, and decoded output generation
Develop and mature the ML inference engine, including:
- Model loading from artifacts
- GPU memory residency strategy
- Batching approach for throughput and latency
- Inference implementation using TensorRT/ONNX or hand-built CUDA inference
- Kernel-level integration with upstream imaging compute
Integrate, extend, and contribute to GPU beamforming where needed
Meet and sustain real-time throughput and latency targets at multi-Gbit/s ingest rates, including deterministic tail latency suitable for closed-loop decoding
Define and own the model serving contract with the ML/AI Lead, including a model update path that avoids service interruption
Build the streaming and storage layer to support both:
- Offline training data access
- Real-time inference data flow
Coordinate with team members to define upstream device acquisition interfaces and downstream BCI application interfaces
Implement observability across all pipeline stages and create benchmarks and regression tests to maintain performance and inference correctness as the system evolves

Requirements

BS/MS/PhD in computer science, electrical engineering, or a related field
8+ years building production real-time or HPC systems
Expert-level CUDA and modern C++; experience writing or extensively modifying GPU kernels for signal processing or imaging workloads
Proven production experience deploying ML inference on GPUs under hard latency constraints, including:
- Transitioning trained PyTorch (or equivalent) models to deployment
- Hands-on use of TensorRT, ONNX Runtime, Triton, or hand-built CUDA inference engines
Demonstrated delivery of real-time data pipelines at multi-Gbit/s rates with tight latency budgets
Strong understanding of data structures, algorithms, and relevant 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 changes, and discuss model architecture trade-offs that affect latency

Technologies

CUDA
C++
PyTorch
TensorRT
ONNX
ONNX Runtime
Triton
Python
FFT
IQ demodulation
SVD-based filtering
Autocorrelation

Benefits

Excellent medical benefits
401(k) with 6% match

Nice to Have

Experience with functional imaging or other high-data-rate imaging modalities
Experience integrating ML inference with upstream real-time 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

Location: Redwood City, CA (onsite)

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