Principal Software Engineer; Medical Imaging

Position

Principal Software Engineer (Medical Imaging)

Los Gatos, CA

614

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Akura Medical, a Shifamed Portfolio Company, is focused on a differentiated approach delivering an effective solution to address the major challenges of venous thromboembolism (VTE). Akura recently announced the first-in-human use of its mechanical thrombectomy platform. Each year VTE affects as many as 900,000 Americans, resulting in about 100,000 premature deaths. To learn more about Akura Medical, please visit

Founded in 2009 by serial entrepreneur Amr Salahieh, Shifamed LLC is a privately held medical device innovation hub focused on the development of novel medical products to address clinical needs in the rapidly evolving fields of cardiology and ophthalmology.

Akura Medical, a Shifamed portfolio company, is seeking an accomplished Principal Software Engineer to lead the design, architecture, and integration of next-generation medical imaging systems. In this senior technical leadership role, you will define technical strategy, drive system-level innovation, and collaborate across disciplines to deliver imaging solutions that meet the highest standards of performance, safety, and compliance. This role emphasizes deep technical expertise and strategic problem-solving, enabling breakthrough advances in patient care without relying on traditional management layers.

This is a full-time, on-site position at our Los Gatos, CA offices.

• Serve as the technical authority for the overall architecture of a 2D/3D/4D imaging viewer (web based and on-device), spanning front-end, services, data, security, and integration layers.
• Define and execute long-term architecture and technology roadmaps for cloud imaging infrastructure, multi-tenant deployments, and web visualization capabilities that align with company objectives and clinical workflows.
• Lead the design of the web 3D imaging front end (e.g., WebGL/WebGPU-based volume rendering, MPR, interactive tools) and its integration with scalable backend APIs and storage.
• Architect secure, highly available cloud infrastructure (e.g., VPCs, containerized services, object storage, databases, messaging) to manage ingestion, routing, storage, and retrieval of large medical image datasets.
• Drive key trade-offs across performance, cost, scalability, latency, image quality, and regulatory constraints; make clear recommendations and decisions at system and subsystem levels.
• Collaborate closely with R&D, clinical, regulatory, cybersecurity, and manufacturing/operations teams to ensure solutions are clinically usable, scalable to production, and compliant with FDA, ISO 13485, and relevant cybersecurity standards.
• Establish best practices for cloud security, data protection, access control, audit logging, and disaster recovery for PHI-containing imaging systems.
• Define technical standards, patterns, and reference implementations for APIs, data models, observability, deployment, and testing of cloud imaging components.
• Mentor and elevate engineers across front-end, backend, and Dev Ops, fostering a culture of technical excellence, architectural rigor, and thoughtful documentation.
• Evaluate and integrate third-party components and services (e.g., DICOM toolkits, web viewers, cloud services, AI services) and manage relationships with key vendors and partners.
• Define and execute long-term architecture and technology roadmaps for cloud imaging infrastructure, multi-tenant deployments, and web visualization capabilities that align with company objectives and clinical workflows.

• Bachelor’s degree in Electrical Engineering, Biomedical Engineering, Computer Science, or related field (advanced degree strongly preferred).
• 12+ years of experience in medical imaging system design, with a track record of leading complex programs from concept to regulatory clearance.
• Proficiency in relevant software and system development tools (e.g., MATLAB, Python, C++, VTK, Qt/QML).
• Proven experience architecting on-prem as well as cloud-based imaging solutions and integrating AI/ML methods for computer…