Senior Machine Learning Engineer

Company Description

Effileap Technologies is a forward-thinking IT and AI research company based in TechnoPark , Trivandrum, India. The company is committed to advancing intelligent systems that drive business transformation and scientific discovery. Effileap’s work spans CRM solutions, productivity tools, and AI-driven automation, with a growing research focus on spatial intelligence, 3D data understanding, and geometric deep learning. The team fosters a culture of experimentation, academic collaboration, and innovation in applied AI systems.

Role Description

Effileap Technologies is seeking a full-time, on-site Machine Learning Research Engineer – 3D Structures to join its advanced AI research division in Thiruvananthapuram. The role involves conducting applied research and development in 3D machine learning, focusing on spatial perception, geometric reasoning, and structure-aware neural network models. The researcher will explore novel algorithms for 3D object recognition, shape reconstruction, simulation data interpretation, and multimodal 3D-2D learning integration. Collaboration with domain experts in AI, physics-based simulation, and computer vision will be essential to advancing Effileap's research initiatives.

Qualifications

• Experience in  3D geometry processing, neural implicit models, or geometric deep learning
• Algorithm Development : Design and implement state-of-the-art deep learning models for 3D tasks such as surface reconstruction, neural rendering, shape generation, and non-rigid registration.
• Geometric Learning : Apply Geometric Deep Learning (GDL) techniques (e.g., Graph Neural Networks, Manifold Learning) to process non-Euclidean data like unstructured point clouds and meshes.
• Neural Implicits : Research and optimize Neural Implicit representations (NeRF, SDF, Occupancy Networks) for real-time rendering, compression, or editing.
• Pipeline Integration : Bridge the gap between traditional geometry processing (remeshing, smoothing, UV mapping) and learnable neural pipelines.
• Optimization : Write custom CUDA kernels or leverage differentiable rendering libraries (e.g., PyTorch3D, Kaolin) to accelerate training and inference of 3D models.
• Strong foundations in  mathematical modeling, optimization, and probabilistic machine learning
• Demonstrated ability to design and evaluate  neural architectures for spatial or structural data
• Proficiency in  Python and machine learning libraries such as PyTorch, TensorFlow, and PyTorch3D
• Experience with  point clouds, meshes, or volumetric representations
• Familiarity with  scientific computing tools (NumPy, SciPy, Open3D, CGAL, or similar frameworks)
• Knowledge in Computer Science, Applied Mathematics, Computational Engineering, or related field
• Prior contributions to  AI research projects, academic publications, or open-source work will be a strong asset.