AI Software Engineer

At our company, we build AI-powered vision systems that enhance safety and decision-making for some of the worlds largest vessels.Our platform processes live video streams from multiple onboard cameras to provide real-time situational awareness, detecting and tracking marine objects, even in low visibility and highly congested environments. These systems directly support navigational decisions and help prevent collisions, reduce human error, and improve operational efficiency. Our systems are already deployed across thousands of vessels and have processed hundreds of millions of nautical miles of real-world data, operating in unpredictable and safety-critical conditions.

This role sits at the intersection of AI and high-performance systems engineering, focused on solving real-world problems under strict constraints. You will work on systems where performance and reliability are critical and where improvements have a direct, measurable impact on real-world safety.This is a senior, systems-focused role with end-to-end ownership over performance and reliability of production computer vision pipelines. You will define optimization strategies, identify bottlenecks across the system, and drive improvements under real-world constraints.What youll doBuild and optimize real-time computer vision pipelines running on edge systems processing live maritime video streams (e.g, NVIDIA Jetson, Triton Inference Server)Take models from research and turn them into production-ready, reliable components deployed on vesselsProfile and improve end-to-end system performance across: multi-camera video ingestion; preprocessing; inference; postprocessingIdentify and resolve bottlenecks across CPU, GPU, memory, and pipeline coordinationMake and justify tradeoffs between latency, accuracy, stability, and resource utilizationDesign 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 gatingBuild and improve observability tools, including logging, monitoring, and debugging workflows for production systemsDefine and maintain clear interfaces between research code and production systemsWork closely with research and backend teams to integrate new models into production systemsContinuously improve system efficiency and reliability under hardware and runtime constraints.Requirements: 5 years of software engineering experience, with a strong focus on systems and performanceHands-on experience working with computer vision or deep learning systems in productionStrong programming skills in Python and/or C Experience working with edge or embedded systems (e.g., NVIDIA Jetson platforms)Strong understanding of system bottlenecks, including CPU, GPU, memory, and latency constraintsStrong intuition for profiling-driven optimization and performance tuningExperience debugging complex systems and reasoning about behavior in real-world, noisy environmentsStrong advantageExperience working with edge or embedded systemsExperience working with custom high-performance data or inference pipelinesFamiliarity with multi-sensor fusion (e.g., combining vision with radar or other signals)Experience deploying and maintaining ML models in production environmentsExperience with low-level optimization and/or C performance tuningProven experience optimizing model inference (e.g., TensorRT, ONNX Runtime, quantization, pruning, or similar techniques).This position is open to all candidates.