Machine Learning Engineer

Machine Learning Engineer (2D→3D 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:Reconstructing 3D anatomical structures from orthogonal 2D X-rays, andBuilding 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 2D→3D 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 analysisResponsibilities: • Architect and train models for 2D→3D 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 documentationRequirements (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 skillsBonus (Big Plus): • Experience with NeRFs, implicit neural representations, depth inference, or differentiable rendering • Experience building autosuggestion systems, ranking models, or intent prediction in complex workflowsWhy 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 marketsHybrid role with periodic clinical onsite work. Compensation: ₹18–24 LPA + ESOPs.