Lead the end-to-end realtime pipeline for a functional ultrasound imaging platform, owning architecture, performance, and integration with BCI applications.
Compensation
Competitive salary and meaningful equity.
Responsibilities
Own the end-to-end realtime path: raw RF data ingestion, beamforming, processing, ML preprocessing, model inference, and decoded output.
Set the technical direction for the realtime stack and represent it in architecture decisions across the company.
Build the ML inference engine: model loading, GPU memory residency, batching, TensorRT/ONNX or hand-built CUDA inference, and kernel-level integration with upstream imaging compute.
Integrate, extend, and contribute to GPU beamforming.
Hit and hold realtime throughput and latency targets at multi-Gbit/s ingest with deterministic tail latency.
Requirements
BS/MS/PhD in computer science, electrical engineering or a related field
Nice to have: signal processing for imaging or RF
Nice to have: functional ultrasound
Nice to have: integrating ML inference with upstream realtime compute
Build and run the industrial edge platform for Factbird’s IoT devices, including embedded Linux, Rust components, Yocto distribution, OTA updates, and vision inference.
Compensation
Competitive salary: We offer compensation that reflects your skills, experience, and impact.
Build next-generation green intelligent computing infrastructure and drive breakthroughs in performance and energy efficiency of AI clusters through innovation.
Responsibilities
Build next-generation green intelligent computing infrastructure, driving continuous breakthroughs in performance and energy efficiency of AI clusters through innovative technology.
Participate in cutting-edge sustainable computing technology research and apply results to large-scale AI infrastructure scenarios.
Conduct cutting-edge research in sustainable computing and green AI, exploring energy efficiency optimization solutions for large-scale AI training and inference scenarios.
Design and build fine-grained energy consumption analysis systems to achieve precise energy monitoring, decomposition, and modeling from cluster, node, and device to task levels.
Develop dynamic frequency and voltage scaling (DVFS), resource management, and intelligent scheduling optimization algorithms for CPU/GPU/NPU resources to improve overall system energy efficiency.
Develop and integrate computer vision and edge AI solutions for Zebra’s AI offerings and SDK platforms, collaborating with researchers and engineers to deploy models on mobile and embedded devices.
Responsibilities
Implement, refactor, and integrate computer vision algorithms and deep learning inference modules into production-grade SDKs and client applications.
Build efficient preprocessing pipelines (filtering, thresholding, perspective transformation, normalization) and post-processing modules using OpenCV and modern C++.
Assist in deploying trained models onto target enterprise devices (e.g., Android mobile computers, edge gateways) utilizing frameworks such as ONNX Runtime, TFLite, or Qualcomm SNPE/QNN.
Measure and profile algorithm latency, memory utilization, and inference accuracy across various hardware platforms.
Support dataset curation, synthetic data generation, and ground-truth validation for OCR, document parsing, and barcode/package recognition tasks.
Lead the architectural design and strategic vision for hybrid inference systems, manage a team of AI engineers, and drive the overarching technical roadmap for AI systems.
Compensation
The US base salary range for this full-time position is $192,100.00 - $249,600.00.
Responsibilities
Architect the Hybrid AI Vision: Lead the architectural design and strategic vision for hybrid inference systems, dynamically distributing Large Language Model (LLM) and Vision-Language Model (VLM) workloads across edge computing environments and cloud infrastructure.
Team Leadership & Innovation: Lead, mentor, and inspire a team of specialized engineers working across distributed systems orchestration, inference optimization, and AI compiler engineering.
Design Dynamic Orchestration & Resilience: Oversee the architecture of high-availability orchestration engines that intelligently route inference tasks.
Guide the team in developing cascading inference mechanisms, dynamic model fallback strategies, and robust telemetry to ensure continuous, steady-state inference under varying connectivity constraints.
Requirements
Ph.D. in Computer Science, Computer Engineering, Artificial Intelligence, or a related field
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