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Orca AI builds AI-powered vision systems that enhance safety and decision-making for some of the world’s largest vessels. Our platform processes live multi-camera video to detect and track marine objects in real time, even in low visibility and congested waters. It's deployed on thousands of vessels and has processed hundreds of millions of nautical miles of real-world data.
This is a senior, systems-focused role. You'll own the performance and reliability of our production computer vision pipelines on the edge: define the optimization strategy, find the bottlenecks across the system, and ship improvements with a direct, measurable impact on safety. You'll work close to the hardware.
What you'll do
Build and optimize real-time computer vision pipelines running on edge systems processing liSenior Software Engineer, Computer Vision (Edge / Real Time)ve maritime video streams (e.g, NVIDIA Jetson, Triton Inference Server)
Take models from research and turn them into production-ready, reliable components deployed on vessels
Optimize model inference using techniques such as quantization, pruning, and graph-level optimization (e.g., TensorRT, ONNX Runtime)
Profile and improve end-to-end system performance across: multi-camera video ingestion; preprocessing; inference; postprocessing
Identify and resolve bottlenecks across CPU, GPU, memory, and pipeline coordination
Make and justify tradeoffs between latency, accuracy, stability, and resource utilization
Design and implement robust data and inference pipelines (video -> model -> actionable output for crew)
Develop benchmarking and evaluation workflows to measure performance end-to-end and support release gating
Build and improve observability tools and debugging workflows for production systems
Define and maintain clear interfaces between research code and production systems
Work closely with research and backend teams to integrate new models into production systems
Continuously improve system efficiency and reliability under hardware and runtime constraints
Requirements
5+ years building production systems, with at least 2 in computer vision or real-time video pipelines
Hands-on experience working with computer vision or deep learning systems in production
Strong C++ (modern C++, multi-threaded, real-time) and solid Python
Experience working with edge or embedded systems (e.g., NVIDIA Jetson platforms or similar)
Strong understanding of inference optimization (e.g., CUDA, TensorRT, ONNX, GStreamer/DeepStream) and system bottlenecks, including CPU, GPU, memory, and latency constraints
Strong intuition for profiling-driven optimization and performance tuning
Experience debugging complex systems and reasoning about behavior in real-world, noisy environments
Experience working with custom high-performance data or inference pipelines
Familiarity with multi-sensor fusion (e.g., combining vision with radar or other signals)
Experience deploying and maintaining ML models in production environments
Experience with low-level optimization and/or C++ performance tuning
Proven experience optimizing model inference (e.g., TensorRT, ONNX Runtime, quantization, pruning, or similar techniques)