Senior Software Engineer, Edge & Cloud Platform

illumicell AI is a medical-technology startup developing AI-powered, label-free cell analysis for rapid diagnostics, starting with male fertility. The company has a functional prototype combining a digital holographic scanner with cloud-based AI analysis and is progressing toward FDA submission. The product involves a Linux-based microscope scanner capturing live-cell video and cloud services providing clinical results, aiming to connect device control, image acquisition, data handling, cloud job orchestration, and GPU analysis in a clinical workflow.

• Substantial professional experience building and operating production software systems, owning their behavior, reliability, and delivery.
• Strong Python skills and solid software design, API, data contract, and debugging judgment.
• Experience across device and cloud, service and infrastructure, backend and hardware, or data plane and control plane boundaries.
• Practical understanding of idempotency, retries, state machines, event-driven processing, object storage, concurrency, failure recovery, and observability.
• Strong testing practices including unit, integration, contract tests, and CI/CD.
• Ability to work from incompletely specified goals, defining scope, making decisions, and progressing without complete specifications.
• History of completing difficult integration work beyond design or prototyping.
• Comfortable using coding agents as engineering tools while retaining accountability.
• Clear communication in English with software, ML, hardware, clinical, and product collaborators.
• Five or more years of relevant professional experience usually expected; relevant degree helpful but not required.

Preferred Qualifications:

• Experience with Google Cloud Platform, including GCS, Eventarc, Firestore, Cloud Run, Cloud Run Jobs, Cloud Scheduler, Secret Manager, Artifact Registry, Cloud Logging, and IAM.
• Familiarity with FastAPI, OpenAPI, JSON Schema, Server-Sent Events, or Pydantic-based typed contract tooling.
• Experience with Terraform or OpenTofu, GitLab CI, Workload Identity Federation, Docker, and GPU jobs.
• Knowledge of Linux device software such as NVIDIA Jetson, Raspberry Pi, USB/UVC cameras, V4L2, GPIO, process supervision, local state persistence, or store-and-forward handling.
• Image or video acquisition experience, including FFmpeg, FFV1, lossless data handling, scientific instruments, or GPU pipelines.
• Familiarity with medical-device software, laboratory systems, patient-identity boundaries, IEC 62304, design controls, or evidence-based development.
• Experience with result delivery interfaces like SMB shares, HL7v2/MLLP, FHIR, SMART Backend Services, LOINC, or UCUM.
• Knowledge of TypeScript or modern web frameworks for limited on-device operator interfaces.
• Background in robotics, IoT, industrial automation, scientific instruments, or healthcare software integrating device and cloud services.

• Take responsibility for major edge and cloud product capabilities from problem definition through deployment and testing.
• Build services and libraries for Linux edge devices with explicit state, resilient local storage, and hardware-aware failure handling.
• Design and implement versioned REST, JSON, event, and storage contracts between scanner, operator interface, cloud control plane, and analysis worker.
• Build cloud application logic for scan reservation, admission, job lifecycle, result state, retries, and recovery.
• Handle repeated events and uncertain network conditions through idempotency, conditional writes, immutable artifacts, and explicit state transitions.
• Integrate reconstruction and ML components into a unified acquisition-scoped worker with stable typed interfaces.
• Develop contract, component, integration, emulator, hardware-in-the-loop, and representative-cloud tests covering real failure modes.
• Add structured logs, metrics, alerts, and reconciliation to detect disabled triggers, stalled scans, or duplicate executions.
• Collaborate with ML engineers, cloud infrastructure lead, hardware, clinical, cybersecurity, and regulatory contributors.
• Use coding agents and engineering automation to accelerate implementation, testing, investigation, documentation, and traceability, critically reviewing generated changes.
• Take on adjacent engineering work when necessary to complete product features.

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