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 you’ll 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)

Additional context

The role is primarily system-focused with responsibility for optimizing inference, improving pipeline performance, and ensuring production reliability. Some interaction with models is expected (e.g., quantization, pruning, architecture-aware optimizations), while model development and training are primarily owned by the research team.

You will be working in environments where failures are often caused by real-world conditions rather than clean lab assumptions - such as low visibility, cluttered scenes, and dynamic environments - and where understanding system behavior in production is key to delivering robust solutions.