Software Developer Workflow Orchestration

Siemens Healthineers

1d•$118,300 - $162,668

About The Position

Develop the software intelligence layer that understands, predicts, and orchestrates neuroendovascular procedures. The role transforms procedural data into workflow awareness, automation opportunities, and clinical decision support. The work includes creating workflow engines and modeling endovascular procedures; developing safe human-supervised autonomous and semi-autonomous workflows; integrating real-time machine-learning and sensor inputs; and collaborating with computer vision, robotics, and other teams in an interdisciplinary regulated medical-device environment. The role works in close alignment with development teams in Germany.

Requirements

Master’s degree with at least 3 years of relevant experience, or a PhD, in Robotics, Biomedical Engineering, Computer Science, Data Science, Artificial Intelligence, Systems Engineering, or a related field.
Experience with complex distributed systems, workflow modeling, state estimation, behavior trees, process automation, decision support, or autonomous decision systems.
Strong understanding of software architecture, systems engineering, and real-time systems.
Experience programming in common languages such as Rust, Python, or C++, and with software engineering following DevOps best practices.
Very good written and spoken English.

Nice To Haves

Familiarity with neurovascular interventions, image-guided therapy, angiography, or clinical workflow optimization.
Creativity and a continuous focus on developing innovative ideas for image-guided therapy solutions.
Organizational skills, initiative, and customer orientation.

Responsibilities

Design and implement hierarchical workflow engines to coordinate medical devices and components used in the interventional suite, such as imaging and robotics.
Model endovascular procedure workflows, including phase entry and exit criteria and real-world event handling based on clinical context, system state, and manual physician interaction.
Develop fail-safe workflows and human handover strategies for autonomous and semi-autonomous operation, including safe supervision logic, confidence thresholds, and deterministic failure recovery.
Integrate inputs from real-time multimodal machine-learning models and sensors, such as X-ray imaging and robots, for real-time state estimation.
Partner with computer vision and robotics teams to integrate imaging-derived events and robotic capabilities into the workflow logic architecture.
Collaborate with cross-functional experts in a regulated medical-device environment and align closely with development teams in Germany.