Build and ship AI-native systems for a neobank, using agents, LLMs, retrieval, and automation to improve financial workflows while ensuring reliability, accuracy, security, and user trust.
Applied AI Engineer
📜 Description
- Design, train, evaluate, and deploy vision and multimodal models from prototype through production.
- Apply vision language models and world models to retail problems and assess their performance at scale.
- Build and optimize real-time AI pipelines processing live video across stores and checkouts.
- Improve system efficiency and optimize models for GPU and edge deployment under latency and cost constraints.
- Run experiments, evaluate results, and collaborate with research, platform, infrastructure, and MLOps engineers to bring models into production.
- Review code, contribute to shared codebases, and track advances in computer vision and multimodal AI.
🛠️ Requirements
- 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.
Full job description
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:
What you’ll do
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.
Similar jobs
Search more AI Engineer jobsBuild and ship production AI systems for a neobank, using agents, LLMs, retrieval, and automation to improve financial workflows, reduce manual work, and deliver reliable user experiences.
Build and ship production AI systems for financial workflows, using agents, LLMs, retrieval, evaluations, and automation to improve user experiences, reduce manual work, and strengthen reliability.
Build and ship production AI systems for a neobank, using agents, LLMs, retrieval, and automation to improve financial workflows, reduce manual work, and deliver reliable user and team experiences.
Build and ship production AI agents, LLM workflows, and automation for financial services, improving customer experiences and operations while ensuring systems are reliable, secure, and useful.
