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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and optimizing real-time computer vision pipelines for edge systems, with a strong focus on model inference optimization and system performance profiling. Proficient in integrating research models into production environments while ensuring reliability and efficiency under various constraints.
Highest-signal resume keywords
C++ ProgrammingPython ProgrammingComputer Vision SystemsInference OptimizationEdge Systems Development
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Real-Time Video PipelinesModel Inference OptimizationQuantizationPruningCUDATensorRTONNXGStreamerMulti-Threaded ProgrammingPerformance Tuning
Soft Skills
Problem SolvingCollaborationDebugging Complex Systems
Tools & Technologies
NVIDIA JetsonDeepStreamMulti-Sensor Fusion
Industry Keywords
Maritime Video StreamsProduction SystemsData PipelinesPerformance BenchmarkingSystem Bottlenecks
Tech Stack
Tools & technologiesPythonC++
About the role
Key responsibilities & impact- Build and optimize real-time computer vision pipelines running on edge systems processing live maritime video streams
- Take models from research and turn them into production-ready, reliable components deployed on vessels
- Optimize model inference using quantization, pruning, and graph-level optimization
- Profile and improve end-to-end system performance across multi-camera video ingestion, preprocessing, inference, and 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 from video to 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
What you’ll need- 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
- Strong advantage: Experience working with custom high-performance data or inference pipelines
- Strong advantage: Familiarity with multi-sensor fusion (e.g., combining vision with radar or other signals)
- Strong advantage: Experience deploying and maintaining ML models in production environments
- Strong advantage: Experience with low-level optimization and/or C++ performance tuning
- Strong advantage: Proven experience optimizing model inference (e.g., TensorRT, ONNX Runtime, quantization, pruning, or similar techniques)