We are a Site Intelligence company transforming how industrial businesses operate. Our platform turns your existing infrastructure into a source of actionable operational intelligence, enabling teams to see what's happening, understand why, and improve continuously. By surfacing the operational signals that reports miss, we help the world's largest logistics, manufacturing, and warehousing operators stay one step ahead, making operations visible and driving proactive safety. We're a fast-growing, high-agency team building category-defining products by leveraging the latest technologies in computer vision and AI.
Industry leaders like DHL, Amazon, and Tesla trust Protex AI to drive measurable safety improvements, achieving an average 64% risk reduction within just three months of deployment. Operating in 20+ countries, Protex is the go-to safety partner for Fortune 500 manufacturing and logistics enterprises, transforming workplace safety with real-time, AI-powered insights.
In-Office Expectation: Hybrid – 2 days/week in-office
Employment Type: Full-time (Direct Hire)
Please note that this is not an open position for contractors or remote work.
About the Team
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.
What You’ll Do
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.
You will:
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.
What You’ll Need
Bsc in Computer Science, Engineering or other relevant STEM
5+ years of software or ML engineering experience, including hands-on work on computer vision or machine learning systems in production.
Hands-on experience with deep learning for computer vision, from training to deploying optimised models (e.g. PyTorch, TensorRT, ONNX).
Strong Python skills, and experience building data, training or deployment pipelines on cloud infrastructure (AWS preferred), with containers and infrastructure as code.
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.
Experience debugging production issues end to end
Experience driving best practices in testing, reproducibility and code review, and mentoring other engineers.
Product sense and clear communication: you connect model quality to client outcomes, write clear design docs, and translate strategic goals into actionable engineering plans.
Nice To Have
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.
Protex AI is an inclusive and equal opportunities employer. We are committed to creating an equitable workplace for everyone regardless of gender, civil status, family status, sexual orientation, religion, age, disability, education level, or race.