Senior Machine Learning Engineer

1 week ago

India Effileap Technologies Full time

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.

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