Job description

Everseen : A leader in vision AI solutions for the world’s leading retailers. The Role Reporting to the VP of Engineering who leads the development of our next generation platform, you will help build the next generation platform that powers Everseen’s products. This is hands-on work at the core of what we ship. You will design, train and take models into production that run against live retail video at massive scale. You will work with the latest advances in vision and multimodal AI, including vision language models and world models, and the systems that make them run in real time. This is a high impact role that will enable you to take on complex problems and see your work reach thousands of stores and impact customer journeys in real time. This role will allow you to take your career to the next level, working alongside industry experts and scaling a successful global organization. Our Technology Stack Our engineering teams at Everseen have the opportunity to work with and develop skills across a modern, high-performance tech stack: Languages: Python, C/C++, CUDA (for GPU-accelerated computing) AI/ML & Computer Vision: PyTorch, OpenCV, TensorRT, ONNX Tools & Infrastructure: Linux, Docker, Kubernetes, Git, and CI/CD pipelines Data & Streaming: Real-time video processing, RTSP/video streaming, and cloud platforms What you’ll do Design, train, evaluate and own vision and multimodal models for Everseen’s next generation platform, and take them from prototype through to production. Apply vision language models and world models to real retail problems, and understand what holds up at scale and what does not. Design and implement targeted solutions to optimize system efficiency, real-time performance, and output. Build and optimize real-time vision AI pipelines that process live video across thousands of checkouts and stores. Partner with research and platform engineers to move new model architectures into production under tight latency and cost constraints. Run experiments, measure results honestly, and let the data decide what ships. Optimize models for GPU and edge deployment using tools such as TensorRT and ONNX, trading off accuracy against speed and footprint. Contribute to and collaborate on the shared codebase, review other engineers’ work, and help raise the bar across the team. Keep up with new research in computer vision and multimodal AI, expand on experimental results presented by others and successfully transition from research to production. Collaborating With You will work closely with the applied AI, platform and infrastructure engineers, MLOps engineers and product managers, along with the operations teams that deploy our software to customers. You will also work with our hardware and cloud partners such as NVIDIA and Google as new models move from the lab into stores. Profile and Skills 3-5 years building and shipping machine learning or computer vision systems, with real time spent taking models into production rather than only doing research. Working knowledge of vision language models (VLMs) and world models, and a clear sense of where they help and where they do not. Strong programming skills in Python and hands-on experience with a modern deep learning framework such as PyTorch. Solid grounding in computer vision and deep learning fundamentals, including training, evaluation and debugging of real models on messy data. Deep understanding of computer vision and deep learning fundamentals including tuning, training, evaluation and debugging of real models on messy data. Experience building real-time vision AI applications is a strong advantage. Familiarity with model optimization and deployment tooling such as TensorRT, ONNX, CUDA and GPU inference. Comfortable working with large scale video or image data and the infrastructure around it. Solid understanding of software engineering principles, version control (Git), and CI/CD pipelines. Ability to integrate and maintain strict standards of code and model quality for easy long-term maintenance. A results-first mindset. You care whether the model works in the store, not just on the benchmark. Excellent communication skills coupled with the ability and desire to work independently in a fast moving, delivery focused team. You enjoy solving complex problems creatively. Bachelor’s or Master’s degree in Computer Science, Machine Learning, Engineering or a related field, or equivalent practical experience. About Everseen Shrink and inefficiency cost global retailers more than a hundred billion dollars each year. But Everseen is changing that equation. We are a leader in vision AI, transforming business operations for global retailers by reducing shrink, improving operations, and delivering a better customer experience. Supporting 11 of the top 20 global retailers, Everseen’s AI innovations help retailers save millions of dollars every month. Its flagship product, Evercheck, is deployed in 10,000 retail stores and 150,000 checkouts globally. Everseen operates at unmatched scale, processing over 15 million transactions, and more than six petabytes of video data, daily. Trusted by major food, drug, mass, and specialty retailers around the world - including Kroger, Meijer, and Woolworths - we also partner with leading hardware, AI, and cloud computing providers such as Google, NVIDIA, NCR, and Dell. Founded in 2007 and headquartered in Cork, Ireland, Everseen has over 700 employees globally, with a European headquarters in Cork, Ireland, a U.S. headquarters in Miami, and hubs in Romania, Serbia, India, Australia, and Spain. We are a dedicated team of inventors, research scientists, engineers, AI experts and retail industry veterans. Our culture is built on accountability, collaboration, and high standards. We take ownership of outcomes, prioritise our customers, operate as one team, move quickly with purpose, and continually raise the bar in everything we deliver. Our Culture Our culture is built on ownership and accountability, we own outcomes, not tasks. We stay relentlessly customer-first, letting their needs shape our priorities and decisions. We work as one team: we challenge ideas, align quickly, and commit together. We move with pace, without compromising on simplicity or quality. And every day, we raise the bar, strengthening our talent, pushing innovation forward, and maximizing our impact. Our Values We OWN THE OUTCOME. No silos, no excuses. We ARE CUSTOMER-FIRST. They guide our decisions. We ARE ONE TEAM. Debate, decide, commit. We MOVE WITH PACE. With simplicity and quality. We RAISE THE BAR. On talent, innovation and impact. Our Commitment Everseen is committed to creating an environment where everyone can succeed. Our employees should feel a sense of belonging, have an opportunity to grow their careers, and feel free to be their most authentic selves. Everseen takes great pride in the diversity of its global workforce, and insists upon a safe, inclusive workplace where our differences are our collective strength. We treat each other with dignity, and respect, and require all employees, officers, and directors to seek to understand the importance and value to Everseen of diversity, and inclusion. Everseen is committed to creating a safe environment for all employees and has a zero tolerance policy for bias and discrimination of any kind. Our work environment is one without offensive, hostile, or intimidating conduct, whether verbal, written or physical, in nature. Everseen will not tolerate prejudice or discrimination of any kind including without limitation, where based on aspects such as, race, colour, sex, gender, religion, age, family status, disability of any kind, sexual orientation.

Requirements

Must-haves

Everseen : A leader in vision AI solutions for the world’s leading retailers.
This role will allow you to take your career to the next level, working alongside industry experts and scaling a successful global organization.
Our Technology Stack
Our engineering teams at Everseen have the opportunity to work with and develop skills across a modern, high-performance tech stack:
Languages: Python, C/C++, CUDA (for GPU-accelerated computing)
AI/ML & Computer Vision: PyTorch, OpenCV, TensorRT, ONNX
Tools & Infrastructure: Linux, Docker, Kubernetes, Git, and CI/CD pipelines
Data & Streaming: Real-time video processing, RTSP/video streaming, and cloud platforms
What you’ll do
Design, train, evaluate and own vision and multimodal models for Everseen’s next generation platform, and take them from prototype through to production.
Apply vision language models and world models to real retail problems, and understand what holds up at scale and what does not.
Design and implement targeted solutions to optimize system efficiency, real-time performance, and output.
Build and optimize real-time vision AI pipelines that process live video across thousands of checkouts and stores.
Partner with research and platform engineers to move new model architectures into production under tight latency and cost constraints.
Run experiments, measure results honestly, and let the data decide what ships.
Optimize models for GPU and edge deployment using tools such as TensorRT and ONNX, trading off accuracy against speed and footprint.
Contribute to and collaborate on the shared codebase, review other engineers’ work, and help raise the bar across the team.
Keep up with new research in computer vision and multimodal AI, expand on experimental results presented by others and successfully transition from research to production.
Collaborating With
Profile and Skills
3-5 years building and shipping machine learning or computer vision systems, with real time spent taking models into production rather than only doing research.
Working knowledge of vision language models (VLMs) and world models, and a clear sense of where they help and where they do not.
Strong programming skills in Python and hands-on experience with a modern deep learning framework such as PyTorch.
Solid grounding in computer vision and deep learning fundamentals, including training, evaluation and debugging of real models on messy data.

Nice-to-haves

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Application prep

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Keywords to mirror

aipythonmldockerkubernetesci/cdmachine learning

Platform: lever
Effort: quick

Benefits

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Vision