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).
