Senior Android Developer (with Kotlin)

Who we are

We are looking for an experienced software engineer to join a research and innovation team exploring the application of Physical AI, autonomous systems and connected drones.

The role will focus on developing and proving new capabilities that connect autonomous or remotely operated physical devices with cloud, edge and enterprise platforms. Initial research use cases include crowd monitoring and public safety, emergency-service applications, and autonomous parcel delivery, with scope to explore additional applications as the technology develops.

This is primarily a backend and integration-focused engineering role, although the target environment includes Android-based devices and applications. The successful candidate will therefore bring strong experience of Android development, Kotlin and modern API/service development, ideally including Ktor.

You will work in a research environment where requirements will evolve rapidly and where experimentation, prototyping and technical problem solving are as important as production software engineering.

What you'll be doing

• Design and develop software supporting Physical AI and autonomous-system research use cases
• Build backend services and device-side components using Kotlin, with Ktor highly desirable
• Develop and integrate Android-based applications and services supporting connected devices, drones and edge-computing platforms
• Design and implement RESTful APIs, WebSockets and event-driven interfaces between devices, edge platforms and cloud services
• Integrate with drone platforms, SDKs, telemetry services, sensors, cameras and other physical devices
• Develop capabilities for real-time ingestion and processing of telemetry, location, sensor, image and video metadata
• Support command-and-control interactions between applications and autonomous or remotely operated devices
• Prototype new use cases quickly, taking concepts from initial experimentation through to working demonstrators and proofs of concept
• Investigate the use of AI/ML and computer vision services within physical and autonomous-system workflows
• Design solutions capable of operating across device, edge and cloud environments, including scenarios with intermittent or constrained connectivity
• Implement appropriate authentication, authorisation, encryption and secure communications between devices and services
• Develop automated unit, integration and API testing and contribute to CI/CD pipelines
• Diagnose complex problems spanning applications, networks, devices and cloud infrastructure
• Produce clear technical documentation covering architecture, APIs, experiments and research findings
• Collaborate with researchers, software engineers, network specialists, AI specialists and external technology partners

What you'll bring along

• BSc/MSc in Computer Science or related field
• Minimum 6+ years as an Android Developer
• Kotlin development is a must
• Android application and service development, including Android SDK and Android lifecycle concepts
• Backend development using Kotlin; experience with Ktor would be particularly valuable
• Design and implementation of REST APIs and asynchronous/event-driven services
• HTTP, WebSockets and common messaging patterns
• Concurrent and asynchronous programming using Kotlin Coroutines
• JSON and common data serialisation approaches
• Integration with third-party SDKs and APIs
• Git and modern source-control practices
• Automated testing, including unit and integration testing
• CI/CD and contemporary DevOps practices
• Containerisation using Docker
• Experience deploying applications or services to a major cloud platform such as AWS, Azure or Google Cloud
• Strong troubleshooting skills across distributed applications and services

• Desirable Skills:
• Drone/UAV development, including integration with drone SDKs, flight controllers or ground-control systems
• MAVLink, PX4, ArduPilot, DJI SDKs or similar drone/autonomous-system technologies
• Edge computing and edge AI
• Computer vision and video analytics
• Integration with AI/ML inference services
• Real-time video streaming technologies such as WebRTC or RTSP
• MQTT, Kafka or other event/messaging technologies
• GPS/GNSS, geospatial data and mapping technologies
• IoT device integration and telemetry
• 4G/5G connectivity and mobile-network concepts
• Linux and embedded/edge computing environments
• Kubernetes or container orchestration
• Observability technologies including logging, metrics, tracing and distributed monitoring
• PostgreSQL, NoSQL or time-series databases
• Python, particularly for AI/ML integration, prototyping or data processing
• Security, Safety & Responsible AI
• Because the research involves physical devices, cameras, location information and potentially public-safety applications, the successful candidate should understand the importance of building security, privacy, safety and responsible AI principles into solutions from the outset