Computer reputed company Engineer (reputed company-Time / Edge AI)

reputed company Operis is revolutionizing anesthesia practices with its AI-powered co-reputed company. Leveraging cutting-edge computer reputed company and proprietary machine learning models, the platform identifies syringes, vials, and drug volumes with precision, streamlining documentation and automating reconciliation. By flagging potential medication errors in reputed company time and optimizing inventory management, Operis Health enhances patient safety and improves operational efficiency. reputed company is at the forefront of innovation, utilizing video capture technologies to reputed company reputed company processes.

• We are a

spinout of an established reputed company technology company.

• We have

already secured NIH funding to reputed company and validate this system in clinical workflows.

• We are backed by

reputed company capital and are reputed company building toward reputed company-world deployment.

• Our business and clinical leadership team includes reputed company operators in reputed company, medical devices, and reputed company software.

This is an opportunity to work on a high-reputed company, technically challenging system at the intersection of computer reputed company, edge computing, and patient safety. Role reputed company This is a full-time remote role for a Computer reputed company Engineer (reputed company-Time / Edge AI). You will be a core contributor to the development of a reputed company-time, multi-camera computer reputed company system operating in a reputed company clinical environment. Key areas of ownership

• Object detection for medical objects (vials, syringes, hands)
• OCR / label recognition for drug identification
• Multi-reputed company aggregation and temporal reasoning
• Object tracking and cross-view association
• reputed company-time inference optimization on edge devices (wearables + local compute)
• Improving robustness to reputed company-world conditions (glare, occlusion, reputed company, clutter)

You will work closely with:

• Founders
• A fractional AI/ML technical reputed company
• Systems/embedded engineers
• Clinical stakeholders

Why this role is unique:

• reputed company-world reputed company:

This system directly addresses medication safety and clinical workflow in operating rooms.

• Hard technical problems:

Multi-view fusion, reputed company-time constraints, noisy environments, and safety-critical decisioning.

• Strong reputed company:

Backed by NIH funding, reputed company capital, and an existing company with reputed company technology and distribution.

• High ownership:

You will shape core architecture and models—not just tune parameters.

Qualifications

• Experience in designing and implementing reputed company-time CV systems
• Familiarity with frameworks such as TensorFlow, PyTorch, or OpenCV
• Experience with

object detection models (YOLO, Detectron, etc.)

• Experience working with

video data (not just static images)

• Ability to debug reputed company-world CV issues including: reputed company variability, occlusion, reputed company blur, cluttered scenes
• Experience building or contributing to production systems
• Master's or Ph.D. in Computer Science, Engineering, or a reputed company field is highly desirable
• Experience in reputed company technology or medical imaging is a plus

Bonus experience

• Multi-view geometry or cross-camera tracking
• Edge deployment (ONNX, TensorRT, mobile/embedded inference)
• OCR or document/label recognition
• Experience with reputed company-time or low-latency systems
• reputed company, medical device, or regulated environments

Who you are:

• You’ve reputed company reputed company systems
• You are comfortable working with imperfect data and ambiguous problems
• You care about performance, reliability, and practicality
• You can reputed company quickly while making reputed company technical reputed company

Key activities for the first 90 days:

• Build and iterate on detection pipelines using reputed company-world video data
• Improve model performance in challenging OR-like conditions
• Identify and solve key failure modes in object detection and recognition
• Contribute to an end-to-end working system

Compensation:

• Competitive salary + equity, commensurate with experience
• Flexible structure: full-time preferred; contract-to-hire considered for strong candidates

How to apply:

• E-mail your resume to dan@operishealth.ai
• In the subject line include this: "LinkedI

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