Role Description We're looking for a hands-on and forward-thinking Machine Learning Engineer. In this role, you will be supported by a dedicated team, equipped to aid your success and bolster our rapid growth. At Critical, we deliver software solutions and consulting in complex, business-critical environments aimed at assisting our clients in achieving their business objectives through cutting-edge software solutions. The ideal candidate will design, build, and deploy machine learning models and pipelines that solve real business problems across various departments. You will work hands-on across the full ML lifecycle — from data preparation and model development to deployment and monitoring — collaborating closely with stakeholders to turn their needs into robust, production-ready solutions. Qualifications - Bachelor's or Master's in Computer Science, AI, Machine Learning or a related field. - 5+ years of experience in software development, with hands-on experience building and deploying machine learning models. - Solid coding skills (Python and relevant ML frameworks) and experience with the ML lifecycle from data to production. - Clear oral and written communication skills for working with teammates and stakeholders. - Practical, detail-oriented approach to debugging and improving models and pipelines. - Ability to manage your own tasks and prioritize effectively, even with some ambiguity. - A natural interest in ML techniques — old and new — and how they apply to real business problems. Requirements - Design, train, and evaluate machine learning models to address specific business problems. - Build and maintain data pipelines and infrastructure to support model development and deployment. - Deploy ML models into production and monitor their performance, reliability, and drift over time. - Identify and resolve technical issues, bugs, and blockers as they arise during development and deployment. - Work closely with team members across various departments to understand their data, processes, and needs, and adapt solutions accordingly. - Iterate on deployed models to keep them accurate, efficient, and useful as needs evolve. Benefits - Private health insurance - Employee Assistance Programme (mental health, legal, financial support) - Home office support - Extra holidays: 2 additional days after year one, more as time goes on - Extra parental leave: 2 additional months fully paid - Gradual return-to-work support: We'll help you ease back from long breaks - Sabbatical programme for long-term employees - Training, mentorship, and growth opportunities: we'll invest in where you want to go next

Role Description - Design and implement scalable model serving platforms for both batch and real-time inference - Build model deployment pipelines with automated testing and validation - Develop monitoring, logging, and alerting systems for ML services - Create infrastructure for A/B testing and model experimentation - Implement model versioning and rollback capabilities - Design efficient scaling and load balancing strategies for ML workloads - Collaborate with data scientists to optimize model serving performance Qualifications - 7+ years of software engineering experience, with 3+ years in ML serving/infrastructure - Strong expertise in container orchestration (Kubernetes) and cloud platforms - Experience with model serving technologies (TensorFlow Serving, Triton, KServe) - Deep knowledge of distributed systems and microservices architecture - Proficiency in Java and experience with high-performance serving - Strong background in monitoring and observability tools - Experience with CI/CD pipelines and GitOps workflows

• Research, develop and deploy advanced machine learning solutions for image analysis, image processing, object detection and segmentation. • Evaluate technical challenges and determine the most effective ML approach, whether developing custom models or adapting state-of-the-art solutions. • Explore emerging technologies and techniques to continuously improve product capabilities. • Design, develop and maintain robust software solutions using modern software engineering practices. • Integrate machine learning algorithms into high-performance desktop applications. • Contribute to the wider C++ application architecture and codebase. • Participate in code reviews and contribute to development best practices. • Profile and optimise model execution for CPU and GPU acceleration. • Ensure efficient processing of large-scale 3D and 4D imaging datasets. • Develop scalable and maintainable solutions suitable for production environments. • Provide technical guidance, mentoring and support to other software engineers and test team members. • Contribute to technical investigations and architectural decisions. • Share knowledge and promote engineering excellence across the team. • Work closely with software engineers, scientists, product managers and domain experts. • Support product development from concept through to deployment. • Occasionally travel to other Oxford Instruments sites and customer locations when required.

• You will work on the backend services behind our route-planning estimator and other operational estimators (e.g., process durations, driver availability) and ensure their stability and maintainability. • You take responsibility for code quality and structure in our ML repositories (reviews, refactorings, architecture). • You work closely with our Data Scientists and reliably bring models and feature pipelines into production. • You develop and operate the associated data and training pipelines in Databricks. • You build and maintain CI/CD pipelines in Azure DevOps – including automated tests and deployments. • You ensure that our systems run reliably in the cloud environment, analyze production incidents and derive sustainable improvements for code and processes.