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 do
Build 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 vessels
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, including logging, monitoring, 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.
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 do
Build 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 vessels
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, including logging, monitoring, 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 of software engineering experience, with a strong focus on systems and performance
Hands-on experience working with computer vision or deep learning systems in production
Strong 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 constraints
Strong intuition for profiling-driven optimization and performance tuning
Experience debugging complex systems and reasoning about behavior in real-world, noisy environments
Strong advantage
Experience working with edge or embedded systems
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).
5+ years of software engineering experience, with a strong focus on systems and performance
Hands-on experience working with computer vision or deep learning systems in production
Strong 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 constraints
Strong intuition for profiling-driven optimization and performance tuning
Experience debugging complex systems and reasoning about behavior in real-world, noisy environments
Strong advantage
Experience working with edge or embedded systems
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).
This position is open to all candidates.
