AI ML Software Developer Medical Imaging

Neuranics Lab; Gurugram District; On-site; INR 1,500,000 - 2,700,000; Full time

Job summary

Neuranics Lab is seeking an experienced AI/ML Software Developer to design, train, validate and deploy computer-vision models for medical-image analysis. You will work with teams across software, optics, biotechnology and clinical disciplines to deliver reliable diagnostic outputs at the point of care.

You will apply PyTorch or TensorFlow, OpenCV, and edge-deployment techniques, while handling data governance and model validation for regulated healthcare products.

Qualifications

• Bachelors or Masters in Computer Science, AI, Data Science, Biomedical Engineering or related field.
• Strong programming in Python and hands-on experience with PyTorch or TensorFlow.
• Experience with medical images (microscopy, radiology, pathology) and regulated healthcare products.

Responsibilities

• Develop AI models for medical-image classification, detection and segmentation.
• Build algorithms for blood-cell identification, counting and morphological classification.
• Train models for RBC, WBC, platelet and abnormal-cell analysis.
• Integrate AI models with device software, imaging hardware, databases and UIs.

Skills

Python

PyTorch

OpenCV

Medical imaging

Edge deployment

Git

Education

Bachelors/Masters in CS/AI/ Biomedical Eng

Tools

Docker

Linux

TensorRT

ONNX

Job description

About Neuranics

Neuranics is developing next-generation point-of-care diagnostic systems using computational microscopy, multispectral imaging, microfluidics and artificial intelligence. Our platform analyzes blood cells to generate haematology parameters and morphology-based clinical insights directly at the point of care.

Role Overview

We are looking for an experienced AI/ML Software Developer to design, train, validate and deploy computer-vision models for medical-image classification, object detection, instance segmentation and quantitative image analysis.

The candidate must have practical experience working on medical devices, clinical imaging systems or regulated healthcare products. You will work closely with software, optics, biotechnology, electronics and clinical teams to convert microscopic images into reliable diagnostic outputs.

Key Responsibilities

• Develop AI models for medical-image classification, detection and segmentation.
• Build algorithms for blood-cell identification, counting and morphological classification.
• Train models for RBC, WBC, platelet and abnormal-cell analysis.
• Develop semantic and instance-segmentation pipelines using architectures such as U-Net, U-Net++, Mask R-CNN and transformer-based models.
• Build robust image preprocessing, normalization, augmentation and quality-assessment pipelines.
• Perform cell-level feature extraction, object tracking, image registration and focus-quality analysis.
• Curate datasets and develop annotation, review and data-versioning workflows.
• Address class imbalance, rare-cell detection, staining variability and imaging artefacts.
• Define appropriate performance metrics, including sensitivity, specificity, precision, recall, F1 score, IoU and Dice score.
• Conduct model error analysis and investigate false-positive and false-negative predictions.
• Optimize models for real-time or near-real-time inference on edge-computing platforms.
• Integrate AI models with device software, imaging hardware, databases and user interfaces.
• Implement model versioning, traceability, automated testing and performance monitoring.
• Prepare technical documentation for design controls, verification, validation and regulatory submissions.
• Collaborate with pathologists, clinicians and biotechnology teams to define clinically meaningful labels and acceptance criteria.
• Support clinical-data analysis and comparison against reference laboratory analysers.
• Maintain clean, modular, tested and production-ready Python code.

Essential Qualifications

• Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, Biomedical Engineering, Electronics or a related discipline.
• Strong programming expertise in Python.
• Hands‑on experience with PyTorch or TensorFlow.
• Strong understanding of computer vision, deep learning and digital-image processing.
• Practical experience developing classification, object‑detection or segmentation models.
• Experience working with medical images such as microscopy, pathology, radiology, cytology, haematology or fluorescence imaging.
• Relevant experience developing software or AI algorithms for a medical device or regulated healthcare product.
• Understanding of model validation using clinical or experimentally generated datasets.
• Proficiency with OpenCV, NumPy, Pandas, scikit‑learn and related Python libraries.
• Experience with Git, code reviews, debugging and structured software‑development practices.
• Understanding of data privacy, patient‑data handling and traceability requirements.

Preferred Qualifications

• Experience with microscopy-based cell analysis, digital pathology or computational cytology.
• Experience detecting or segmenting blood cells, nuclei, platelets or cellular morphology.
• Knowledge of brightfield, darkfield, fluorescence or multispectral imaging.
• Experience deploying models using ONNX, TensorRT, CUDA or NVIDIA Jetson platforms.
• Familiarity with Docker, Linux and REST APIs.
• Experience with experiment-tracking and data-versioning tools such as MLflow, Weights & Biases or DVC.
• Knowledge of statistical method comparison, BlandAltman analysis and clinical-performance evaluation.
• Familiarity with medical-device standards and frameworks such as:
• ISO 13485
• IEC 62304
• ISO 14971
• IEC 62366
• Good Machine Learning Practice
• CDSCO, FDA or CE regulatory requirements

Technical Skills

• Python
• PyTorch or TensorFlow
• OpenCV and scikit-image
• U-Net, U-Net++, Mask R-CNN, YOLO and vision transformers
• Image classification, detection and segmentation
• Medical-image preprocessing and augmentation
• Model optimization and edge deployment
• CUDA, TensorRT and ONNX
• Git, Linux and Docker
• SQL or NoSQL databases
• Statistical analysis and model validation

What We Are Looking For

• Strong problem-solving and analytical abilities.
• Ability to convert clinical requirements into measurable engineering specifications.
• Attention to accuracy, reproducibility and patient-safety considerations.
• Ability to work with imperfect, imbalanced and limited medical datasets.
• Clear communication across multidisciplinary engineering and clinical teams.
• Ownership of the complete model lifecycle from dataset preparation to deployment and post-deployment monitoring.

Why Join Neuranics?

• Build AI models that directly contribute to clinical decision-making.
• Work with real-world microscopy and clinical datasets.
• Collaborate across AI, optics, robotics, microfluidics and biotechnology.
• Participate in the development and commercialisation of an innovative point-of-care diagnostic platform.
• Contribute to making laboratory-quality diagnostics more accessible.