CargoSeer Senior LI Engineer

We’re looking for a senior AI/ML engineer with a strong software engineering background, hands-on experience with LLMs and modern AI systems, and the ability to design and build end-to-end AI architectures to join our family. You will contribute to developing AI-driven software that revolutionizes customs control processes, significantly speeding up cargo X-ray inspections, and improving fraud detection.

You may be a great fit if you enjoy turning complex AI ideas into scalable, production-grade products and working across models, data and software architecture.

Moreover, you may have experience across roles such as Machine Learning Engineer, Algorithm Tech Lead, Applied Research Scientist, Computer Vision Algorithm Engineer or AI Systems Engineer.

Job Description

• Research, evaluate, and integrate AI models, LLM-based components, computer vision models, and reasoning workflows into scalable production systems.
• Work with customs-related data, documents and, where relevant, X-ray imaging data, applying relevant SOTA methods.
• Develop multimodal AI approaches for multiple data sources, including textual, numerical, document-based, and visual data.
• Collaborate closely with software engineers, data engineers, product teams, and customs-domain experts to turn AI capabilities into reliable production solutions.

A day in the life

• Tackle complex, real-world customs inspection challenges where no “off-the-shelf” solutions exist.
• Build AI systems that accelerate cargo X-ray inspection and uncover fraudulent trade activities by connecting LLM-driven reasoning, structured data, production software workflows, and, where relevant, computer vision inputs.
• Design fusion algorithms across multi-modal inputs: X-ray images, scanned documents, textual declarations, and structured numerical data.
• Contribute production-grade code, and work hand-in-hand with engineering teams to deploy research outcomes in live systems.
• Work with large-scale international customs datasets and annotated X-ray scans
• Collaborate with a cross-functional team of computer vision engineers, data engineers, and domain experts from customs authorities.
• See the broader system context: design AI workflows that connect data ingestion, model inference, LLM-driven reasoning, and customer-facing decisions, while staying mindful of performance, scale, integration points, and real-world policy constraints.

You bring to the table

• 8+ years of industry experience building ML, deep learning or AI systems.
• Strong production software engineering expertise, with the ability to design, build, debug, optimize, and maintain scalable AI-driven systems.
• Hands-on experience developing, integrating, evaluating, or deploying LLM-based systems in production or production-like environments.
• Experience designing end-to-end AI workflows, including data ingestion, model integration, inference, reasoning, deployment, monitoring, and user-facing outputs.
• Track record of building or deploying production-grade ML/AI systems, not only research prototypes.
• Strong Python skills and experience with PyTorch or similar ML/deep learning frameworks.
• Demonstrated ability to work with large-scale, noisy, imbalanced, or domain-specific datasets.
• Understanding of multi-modal AI, including fusion of visual, textual, and numerical data.
• Hands-on experience with computer vision, image processing, or deep learning for visual data is a strong advantage.
• You have a strong technical foundation, deep curiosity for your field, and a driven, ownership-oriented mindset. You are a team player who can push complex technical work forward, challenge assumptions, bring new ideas to the table, and help raise the technical bar for your team.

Experience & Education

• 8+ years of industry experience building and shipping deep learning systems, with a strong focus on AI and LLMs
• B. Sc., M.Sc. or Ph.D. in Computer Engineering or Computer Science

Nice to have

• Experience with AWS/GCP, Docker & Kubernetes, customs/logistics applications, or multi-view visual reasoning