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.