• We’re building Site Intelligence, an AI platform that connects cameras, enterprise systems and operational knowledge to give businesses a real-time understanding of what’s happening across their operations
• Our computer vision technology transforms video into structured operational data, while AI agents turn that data into insights, investigations and actions. We’re helping some of the world’s largest enterprises improve safety, eliminate inefficiencies and increase productivity
• We’ve built a strong foundation in safety. Now we’re expanding into a much bigger opportunity: using AI to fundamentally change how industrial operations are understood, managed and optimised
• The Computer Vision Operations team owns the machine learning lifecycle behind every Protex detection
• We build and run the pipelines that turn video from client sites into curated datasets, train and evaluate our detection models, and optimise and release them to edge devices deployed around the world
• Our current focus is delivering reliable detections to both the existing Safety product of Protex and the rapidly expanding new Operations product
• You will work with other experienced engineers driving continuous improvement of the machine learning lifecycle behind Protex AI’s detections: the data and training pipelines, model evaluation and release, and monitoring in production
• This is a hands-on individual contributor role
• Your influence comes from leading high-impact work across teams, setting the technical standards others build on, championing client needs in technical discussions, and helping the engineers around you grow
• You understand both the product and the technical aspects, and can drive key initiatives to fulfill both
• Shape the work before it is built: partner with product managers in scoping, design and drive the implementation
• Set the technical direction for our ML platform. You continuously learn about the latest trends in computer vision and can balance those with the real-world constraints on compute, storage and costs
• Lead triage and root-cause analysis on the team’s support rotation when detection quality drops at a client site. You build components, tools and processes in a way that prevents issues in production and makes them faster to detect and faster to fix
• Raise the engineering bar: review code as the quality gate, define standards for testing and reproducibility
• Mentor and unblock other engineers through pairing, reviews and enablement sessions
• Act as the technical voice of computer vision with product, infrastructure, application development, the install team, technical support and external partners
• Make key contributions to the computer vision strategy
• Model our core values: urgency, excellence, agency and care

Benefits

• Company Laptop (Choice of Mac or Windows)
• Participation in Employee Share Option Programme
• Monthly Company Events
• Pension Scheme
• Health Insurance & Wellbeing Allowance
• Truly Unlimited Holidays
• Nomad Working for 20 Days
• Remote and Hybrid working opportunities

• Interested in creating a new category with us? We’re looking for high-agency people who thrive in ambiguity, move quickly and care deeply about solving real problems
• Bsc in Computer Science, Engineering or other relevant STEM
• Product sense and clear communication: you connect model quality to client outcomes, write clear design docs, and translate strategic goals into actionable engineering plans
• Strong Python skills, and experience building data, training or deployment pipelines on cloud infrastructure (AWS preferred), with containers and infrastructure as code
• Experience debugging production issues end to end
• A solid grasp of model evaluation and MLOps
• A track record of owning architectural components, leading architecture discussions and driving complex technical projects across teams from design to production, without formal authority
• Hands-on experience with deep learning for computer vision, from training to deploying optimised models (e.g. PyTorch, TensorRT, ONNX)
• Experience driving best practices in testing, reproducibility and code review, and mentoring other engineers
• 5+ years of software or ML engineering experience, including hands-on work on computer vision or machine learning systems in production
• Experience scaling software systems in a startup or high-growth environment
• Experience with video analytics or edge deployment
• Experience with vision-language or foundation models
• Experience with dataset curation, annotation workflows or labelling partners
• Experience using AI coding agents in day-to-day engineering work
• Exposure to data governance practices