Company Description

MYL Instruments is developing SYLVIE, a surgical AI Co-Pilot designed to improve operating room efficiency and support surgical teams. SYLVIE learns real surgical workflows and individual surgeon preferences to provide intelligent, context-aware assistance. By optimizing OR productivity, reducing surgical supply waste, and streamlining staff onboarding, MYL Instruments helps hospitals operate more effectively. The company's mission is to make world-class OR efficiency accessible to hospitals of all sizes, using advanced AI and edge technologies.

Role Description

You will own the end-to-end development of an edge video system deployed in and around operating rooms. The system captures first-person video from multiple wired head-mounted cameras and an SDI source, records simultaneous encrypted streams on an embedded AI computer, displays a selected feed over HDMI with physical-button controls, and securely uploads recordings and communicates to our backend server after each session.

This is a hands-on, high-ownership role spanning embedded Linux, real-time video pipelines, camera and capture-card integration, security implementation, and hardware work, from architecture and prototyping through verification, documentation, and pilot handover.

What You'll Do

Architect and implement a multi-stream video pipeline on edge devices: simultaneous ingest of networked camera streams and SDI feed, multiple-feed independent recording, and low-latency HDMI display of a selected feed with digital zoom and pan/crop. Build the capture nodes: camera integration (V4L2 / vendor SDKs), hardware-accelerated encoding, and reliable low-latency streaming over a dedicated wired network, fully offline. Evaluate and integrate candidate head-mounted cameras and SDI capture hardware; produce compatibility assessments covering interfaces, drivers, latency, and integration risk. Implement the security baseline. Deliver a fully headless system: automatic startup and recovery, physical start/stop, feed-selection, zoom, and directional controls via GPIO, and status LEDs. Integrate remote administration and telemetry: Systems Manager enrollment, application telemetry to backend endpoints, remote software/container/model deployment and rollback support. Ensure the video services preserve GPU and compute headroom for MYL's on-device AI processing pipeline running on the same platform. Conduct verification testing (8-hour continuous recording, interruption recovery, concurrency, latency) and produce complete documentation, deployment materials, and a reproducible-build handover package.

Minimum Qualifications

3+ years of professional experience in embedded systems, video/streaming engineering, or edge computing on Linux. Strong proficiency with embedded Linux: systemd services, udev, headless operation, automatic startup and recovery, Bash scripting. Hands-on experience building video pipelines with GStreamer, FFmpeg, or NVIDIA DeepStream, including multi-stream ingest (RTSP/RTP), hardware-accelerated H.264/H.265 encoding, and simultaneous recording. Direct experience with NVIDIA Jetson platforms (JetPack, NVENC/NVDEC, multimedia API) or closely equivalent embedded GPU/SoC video work. Camera integration experience: V4L2, USB/IP camera SDKs, and driver-level troubleshooting. Solid networking fundamentals: wired network design, streaming protocols, connection monitoring, and automatic reconnection handling. Working knowledge of AWS service integration from device software: REST APIs, S3 multipart uploads with presigned URLs, and retry/resume logic. Practical security implementation skills: TLS/mTLS, certificate management, and disk or file-level encryption on Linux. Strong C/C++ and/or Python; Git; Docker or containerized deployment. Basic embedded hardware skills: GPIO for buttons and LEDs, debouncing, and simple wiring on developer-kit hardware. Ability to work independently through a phased development plan, document thoroughly, and communicate technical risk early and clearly.

Preferred Qualifications

Experience with TPM 2.0 provisioning and hardware-backed key storage. Experience with AWS Systems Manager (SSM) managed-node enrollment and remote fleet administration. SDI capture-card integration (Blackmagic DeckLink, Magewell, AJA). TensorRT or DeepStream deployment experience, or familiarity with resource budgeting for on-device ML workloads. Prior work in medical devices, operating rooms, or other regulated, privacy-sensitive environments. Experience writing formal verification test reports and structured handover/documentation packages. Bilingual (English/French) an asset.