Senior Software Engineer - Computer Vision
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At a glance
What you'll be doing
Senior Software Engineer role focused on building AI-driven computer vision systems for real-world environments. Hands-on position with influence over architecture, engineering standards, and AI tooling adoption. Hybrid work in Galway, Ireland.
- Build fast, reliable C++ software for edge devices
- Develop multi-camera calibration and object tracking
- Optimise vision and inference pipelines on CPU and GPU
- Take models from prototype into stable production
- Work hands-on with cameras and edge hardware to solve problems end to end
- Drive automated testing and better engineering practices
- Mentor engineers and contribute to design and code reviews
Key requirements
Must-have
- Substantial commercial experience in computer vision, robotics, embedded or edge software
- Strong production C++ skills, including concurrency and performance
- Solid grasp of camera geometry, calibration and tracking
- Strong maths background (linear algebra, optimisation, estimation)
- Experience shipping real-time or resource-constrained systems
- Git and CI/CD
- Practical use of AI coding tools
Nice-to-have
- CUDA, TensorRT or NVIDIA edge platforms
- OpenCV, Eigen or Ceres
- Robotics or industrial vision background
Role signals
- Technical focus
- computer vision, edge software, AI-driven systems
- Leadership
- Mentoring
- Architecture / system design
- Indicated in the listing
- Hands-on vs management
- Hands-on
Similar jobs
Full job description
Senior Software Engineer - Computer Vision | Galway, Ireland | Hybrid | Permanent
Hiring on behalf of a growing technology company, we are seeking a Senior Software Engineer to build AI-driven vision systems that run live in busy, real-world environments. It's a hands-on senior role with genuine influence over architecture, engineering standards and how the team adopts AI tooling.
Responsibilities
- Build fast, reliable C++ software for edge devices.
- Develop multi-camera calibration and object tracking.
- Optimise vision and inference pipelines on CPU and GPU.
- Take models from prototype into stable production.
- Work hands-on with cameras and edge hardware to solve problems end to end.
- Drive automated testing and better engineering practices.
- Mentor engineers and contribute to design and code reviews.
Requirements
- Substantial commercial experience in computer vision, robotics, embedded or edge software.
- Strong production C++ skills, including concurrency and performance.
- Solid grasp of camera geometry, calibration and tracking.
- Strong maths background (linear algebra, optimisation, estimation).
- Experience shipping real-time or resource-constrained systems.
- Git and CI/CD; practical use of AI coding tools.
Nice to Have
- CUDA, TensorRT or NVIDIA edge platforms.
- OpenCV, Eigen or Ceres.
- Robotics or industrial vision background.
Benefits
Hybrid working, competitive salary and benefits, and access to modern hardware and tooling.
Reperio Human Capital acts as an Employment Agency and an Employment Business.
Interview prep pack
Grounded in this listing. Use it to prepare examples before you apply.
Your interview focus
Based on this listing, the role is a hands-on senior engineering position focused on building and optimizing AI-driven computer vision systems for real-world, edge device deployments, with significant influence over architecture and engineering standards.
- C++ production expertise·High
- Computer vision and calibration·High
- Real-time/edge deployment·High
- Mentoring and code quality·Medium
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review Key Computer Vision Projects
0–8 minSelect 1-2 recent projects that best demonstrate your experience with C++ and edge device deployment; prepare concise summaries and outcomes.
Refresh Camera Calibration and Tracking Concepts
8–14 minStudy the mathematical foundations and practical implementation of camera calibration and multi-camera tracking.
Prepare Optimization and Deployment Examples
14–20 minList specific techniques and tools you've used to optimize inference pipelines for CPU/GPU and transition models to production.
Summarize Mentoring and Engineering Practices
20–25 minRecall examples of mentoring, code reviews, and driving engineering standards in previous teams.
Draft Role-Specific Questions
25–30 minWrite down 2-3 thoughtful questions about the team's challenges, workflows, and technology stack.
Talking points
, 6 itemsProduction C++ for Edge Devices
Demonstrate your ability to write efficient, reliable, and concurrent C++ code suitable for resource-constrained environments.
Camera Calibration and Multi-Camera Tracking
Showcase your experience with camera geometry, calibration, and tracking, which are central to the role's responsibilities.
Optimizing Vision Pipelines (CPU/GPU)
Highlight your skills in optimizing inference and vision pipelines for performance on both CPUs and GPUs, a key requirement for real-time systems.
Shipping Real-Time Systems
Provide examples of delivering robust, real-time or embedded systems, emphasizing your ability to take prototypes to stable production.
Mentoring and Code Review
Prepare to discuss your experience mentoring engineers and contributing to engineering standards and code quality.
Automated Testing and CI/CD
Demonstrate your practical experience with Git, CI/CD, and automated testing to ensure software reliability and maintainability.
What to research
, 4 itemsRecent Computer Vision Projects
Review your most relevant projects involving computer vision, especially those using C++ and deployed on edge or embedded systems.
Camera Calibration and Tracking Techniques
Refresh your understanding of camera geometry, calibration algorithms, and multi-camera tracking methods.
Performance Optimization for Edge Devices
Prepare examples of optimizing inference pipelines for CPU and GPU, including any experience with CUDA, TensorRT, or similar technologies.
CI/CD and Automated Testing in C++
Be ready to discuss your experience with Git, CI/CD pipelines, and automated testing frameworks relevant to C++ and computer vision.
Questions to ask
, 6 itemsWhat are the main technical challenges currently faced by the team in deploying vision systems to edge devices?
Why ask this? To understand the real-world constraints and priorities of the role.
How does the team approach model optimization and deployment for real-time performance?
Why ask this? To gauge the maturity of the team's processes and tools for production AI systems.
What is the typical workflow for taking a prototype model into stable production?
Why ask this? To clarify expectations around model lifecycle and deployment responsibilities.
How is mentoring and knowledge sharing structured within the engineering team?
Why ask this? To assess opportunities for leadership and professional growth.
What hardware platforms and toolchains are most commonly used for development and deployment?
Why ask this? To ensure alignment with your technical background and interests.
How does the team stay up to date with advances in AI and computer vision tooling?
Why ask this? To understand the company's commitment to innovation and learning.
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