Machine Learning Engineer

Machine Learning Engineer (2D3D Reconstruction & Workflow Intelligence)

OpEase Technologies builds a high-precision, web-based surgical planning platform for orthopedic and spine surgeons. Doctors use OpEase to securely store patient data, upload X-rays, calibrate, measure, and plan surgeries through advanced geometry tools and clinical logic.

Role Overview:

We're hiring an ML Engineer who will own the core AI systems powering OpEase specifically:

1. Reconstructing 3D anatomical structures from orthogonal 2D X-rays, and
2. Building intelligent auto-selection and auto-suggestion logic for measurement and planning tools inside our surgical workflow.

This is not a research-only role. You will be responsible for designing, training, validating, and deploying production-grade ML systems that directly impact surgical decision-making. Clear problem statements and datasets will be provided; you are expected to execute with speed and rigor.

What You Will Build (Very Specific):

A robust 2D3D spine/long-bone reconstruction model using dual-view X-rays (AP + lateral)

Landmark/keypoint detection models for vertebrae, femur/tibia, pelvis, etc.

Heatmap regression networks for anatomical feature extraction

A model-driven auto-selection system that identifies which OpEase tool the surgeon requires based on image context and user behaviour

End-to-end inference pipeline integrated into our MERN + Cornerstone-based viewer

Continuous evaluation pipelines for accuracy, latency, and failure-case analysis

Responsibilities:

Architect and train models for 2D3D anatomical prediction using multi-view geometry, implicit fields, NeRF/DVGO variants, or transformer-based approaches

Build landmark detection modules for calibration, templating, and surgical planning

Design the autosuggestion engine: tool intent prediction, context modelling, clinical-rule integration

Manage data pipelines for X-ray preprocessing, augmentation, versioning, annotation QC, and synthetic dataset generation

Validate models with surgeons; refine based on clinical feedback

Deploy models to production (REST endpoints, ONNX/TensorRT optimization, GPU/CPU fallback)

Maintain experiment logs, metrics dashboards, and detailed model documentation

Requirements (High Priority & Non-Negotiable):

Minimum 4 years of full-time experience in ML/Deep Learning with shipped models in production

Strong experience in computer vision for geometry problems: keypoints, reconstruction, pose estimation, volumetric prediction

Hands-on expertise with PyTorch, multi-GPU training, and advanced optimization techniques

Prior work with DICOM/X-ray/medical imaging OR demonstrably adjacent experience (e.g., industrial CV, robotics perception, pose estimation)

Proven ability to independently take a model from idea dataset training evaluation production

Strong mathematical grounding in 3D geometry, camera models, coordinate transforms, and projection systems

Excellent documentation and communication skills

Bonus (Big Plus):

Experience with NeRFs, implicit neural representations, depth inference, or differentiable rendering

Experience building autosuggestion systems, ranking models, or intent prediction in complex workflows

Why Join:

You will own the foundational AI layer for India's most advanced orthopedic planning platform

Clear, well-scoped problems and direct access to clinicians who use your models

Chance to build category-defining medical AI from the ground up

High ownership, high-impact role in a company scaling rapidly across India and global markets

Hybrid role with periodic clinical onsite work.

Compensation: 1824 LPA + ESOPs.