Real-Time 3D Vision Engineer – On-site MedTech

Cruxx; New Jersey; On-site; USD 120,000 - 170,000; Full time

Job summary

Cruxx, a MedTech company in Central New Jersey, seeks an experienced AI/ML engineer to bridge research and product. You will take segmentation, registration and simulation methods from the lab and make them fast, robust, and deployable in the operating room, while informing the next model with real-world data.

You will split time between training/evaluating models, writing inference code, and building data/evaluation infrastructure to measure impact and reliability for regulatory-ready AI in a

Qualifications

• Three or more years applying deep learning to real computer-vision problems.
• Experience shipping a model from research to a shipped product or deployed system.
• Strong Python and PyTorch; working C++ for inference and integration.

Responsibilities

• Develop and productionise computer-vision and deep-learning models for segmentation, surface reconstruction and registration from live imagery.
• Optimise inference for real-time performance on GPU hardware: quantisation, TensorRT or equivalent, memory and latency budgets that hold under load.
• Build and maintain the data pipeline: capture, annotation, dataset versioning, training, evaluation and deployment.
• Design evaluation that reflects clinical reality: ground-truth protocols, failure-mode analysis, accuracy across patients, lighting and occlusion.
• Document models, data and decisions to the standard a regulator will expect for AI in a medical device.
• Collaborate closely with the navigation software team so models and pipeline are designed together, not bolted on.

Skills

Python

PyTorch

3D vision

Deep learning

Education

MSc in computer vision or ML

PhD in computer vision or ML

Tools

C++

TensorRT

Job description

Cruxx, a MedTech company in Central New Jersey, seeks an experienced AI/ML engineer to bridge research and product. You will take segmentation, registration and simulation methods from the lab and make them fast, robust, and deployable in the operating room, while informing the next model with real-world data.

You will split time between training/evaluating models, writing inference code, and building data/evaluation infrastructure to measure impact and reliability for regulatory-ready AI in a