TomTom

iOS Engineer

• Engineer, automate and continuously improve services supporting TomTom’s global Apple device estate
• Deliver a secure, reliable and consistent macOS experience at scale
• Build maintainable automation for device enrolment, configuration, application delivery, patching, compliance and lifecycle management
• Build integrations and workflows across Jamf Pro, Apple Business Manager, identity, security, asset and service-management platforms
• Make Apple device services easier to operate, measure and support
• Reduce manual effort, deployment lead time and configuration drift across the macOS estate
• Automate, observe, audit and improve enrollment, configuration, software delivery, patching, security compliance and device lifecycle processes
• Establish a scalable Apple endpoint automation model based on Jamf Pro, APIs, version-controlled code, release pipelines and shared engineering standards
• Design and maintain automation for macOS enrolment, provisioning, configuration, application delivery, updates and device retirement using Jamf Pro

macOS, Jamf Automation Specialist

Research Scientist

• Design and evaluate algorithms that turn noisy, incomplete probe data into reliable speed and closure information
• Prototype in Python, then implement in Java in production services and own the change through rollout
• Develop against ground truth and use the results to shape future development
• Evolve existing traffic services used by millions of drivers
• Use AI coding assistants such as Claude Code for exploration, implementation, and analysis, while reviewing outputs and retaining full accountability
• Collaboratively run production systems, including monitoring, incident management, and production support
• Improve traffic services processing 5–6 billion kilometers of driving data every day across 70+ countries

Applied Scientist

AI Engineer

• Design, develop and maintain production AI solutions, including LLM-based applications, retrieval-augmented generation (RAG) pipelines, agentic workflows and ML model services
• Take models and prototypes from applied scientists and turn them into robust, low-latency, cost-efficient services that scale to global traffic
• Define and automate evaluation frameworks, benchmarks and guardrails measuring accuracy, safety, latency and cost
• Monitor models once they are in production
• Build and optimize data pipelines for training, fine-tuning, embedding and inference
• Ensure data is high quality, traceable and handled in line with privacy requirements
• Implement CI/CD for ML, model versioning, experiment tracking and observability on cloud platforms
• Collaborate with product, engineering and science stakeholders to understand requirements, weigh trade-offs and deliver AI solutions meeting customer needs
• Evaluate new models, tools and techniques and share learnings with the wider engineering community

• Research and design how LLMs access and reason over TomTom's structured and geospatial data
• Develop retrieval strategies, tool and API designs, text-to-query approaches, and grounding techniques
• Develop AI-ready representations of location data, including geospatial embeddings, knowledge graphs and structured context formats
• Define benchmarks, factuality and hallucination metrics, and evaluation datasets for location-grounded tasks
• Design, train and fine-tune ML and deep learning models, including LLMs, computer vision, time-series and graph-based methods
• Work with product managers and stakeholders to frame high-impact opportunities as scientific problems with clear success metrics
• Write production-quality code and collaborate with AI and software engineers to move solutions from prototype to production
• Balance accuracy, latency, cost and scalability
• Keep up with research in LLMs, retrieval and agentic systems and adapt techniques to TomTom's problems
• Communicate findings to technical and non-technical audiences and contribute to scientific culture