Software Engineer (Back-end & Computer Vision)

Tasks

• Design and build the back-end infrastructure — REST APIs, cloud services, and data pipelines that connect all system components

• Build and manage system integrations — connecting hardware, external services, and internal components into a cohesive and reliable data flow

• Implement real-time data streaming — from smartglass sensor capture through to server-side processing and response delivery back to the device

• Integrate AI capabilities into the application layer — including language understanding, audio processing, and output validation and scoring pipelines

• Interface with smartglass SDKs and sensor APIs to establish device-to-back-end data pipelines, working from technical documentation provided by our hardware partner

• Connect AI model outputs into application layers — inference endpoints, model versioning, and performance monitoring

• Support computer vision — from architecture development and integration — connecting CV model outputs into the back-end and application layer

• Make pragmatic architectural decisions appropriate for the current prototyping stage

• Set up CI/CD, containerization, and basic observability to keep the team moving fast

• Support integration points between the back-end and Unity-based front-end (nice to have)

Requirements

Must-Have

• Experience designing and building back-end systems from scratch — with a clear understanding of how components fit together and why certain decisions were made

• Comfort working with real-time data flows — aware of the constraints and trade-offs involved in keeping systems responsive under continuous loadHands-on experience bringing

• AI engineering experience — hands-on integration of AI models and services into production or prototype systems, spanning language understanding, audio processing, and output validation and scoring workflows

• System integration experience — connecting hardware, third-party APIs, and internal services across different layers into a coherent, functioning system

• Cloud platform experience (AWS / GCP / Azure) — able to spin up infrastructure, manage storage, and deploy services independently

• Comfort working with hardware SDKs and APIs — able to independently navigate technical documentation and integration guides to establish device-to-server data flows

• Comfortable with Docker and basic CI/CD pipelines

• Self-directed and pragmatic — able to take initiative, own problems end-to-end, and make sensible technical decisions independently in a lean team

• Fluent in English — German is a bonus

Strong Plus

• Database experience — PostgreSQL, NoSQL, object storage (S3 / GCS)

• Unity experience or familiarity with Unity's integration patterns

• Familiarity with semi-supervised learning — leveraging limited or partially labeled data, relevant when annotated wound imaging data is scarce

• Experience with 3D data pre-processing and post-processing — depth map handling, point cloud cleaning, mesh reconstruction, or similar.

• Exposure to multi-modal learning — combining RGB and depth or LiDAR data into unified model inputs