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Senior AI Systems Engineer

Orca AI·Israel·en
Not specifiedFull-timeAI EngineeringMaritime Transportation

Join a company developing AI-powered vision systems for maritime safety. Own the performance, reliability, and production deployment of real-time computer vision pipelines running on edge systems.

Responsibilities

  • Build and optimize real-time computer vision pipelines for live maritime video on edge systems
  • Convert research models into reliable production components deployed on vessels
  • Profile and improve video ingestion, preprocessing, inference, and postprocessing
  • Identify and resolve CPU, GPU, memory, and pipeline coordination bottlenecks
  • Balance latency, accuracy, stability, and resource utilization
  • Design robust video-to-actionable-output data and inference pipelines
  • Develop benchmarking and evaluation workflows for end-to-end performance and release gating
  • Improve logging, monitoring, debugging, and production observability
  • Define interfaces between research code and production systems
  • Collaborate with research and backend teams to integrate new models
  • Improve system efficiency and reliability under hardware and runtime constraints

Requirements

  • 5+ years of software engineering experience focused on systems and performance
  • Production experience with computer vision or deep learning systems
  • Strong Python and/or C++ programming skills
  • Experience with edge or embedded systems such as NVIDIA Jetson
  • Understanding of CPU, GPU, memory, latency, and other system bottlenecks
  • Experience with profiling-driven optimization and performance tuning
  • Ability to debug complex systems and reason about behavior in noisy real-world environments

Nice to have

  • Experience with custom high-performance data or inference pipelines
  • Familiarity with multi-sensor fusion, including vision and radar
  • Experience deploying and maintaining machine learning models in production
  • Low-level optimization or C++ performance tuning
  • Inference optimization using TensorRT, ONNX Runtime, quantization, pruning, or similar techniques

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