As a Deep Learning Engineer specialized in 3D Computer Vision and Reinforcement Learning, you will work on developing, implementing, and optimizing state-of-the-art models that tackle complex challenges in spatial AI and geometric processing. You will be part of a multidisciplinary team pushing the frontiers of 3D representation, algorithmic generation, and autonomous agents, contributing to the research, development, and deployment of large-scale AI systems capable of interpreting, manipulating, and generating complex 3D structures.

Requisite Abilities and Skills:

Relevant Experience

1 to 4 years

Desired Education Qualification

Degree in Computer Science, Applied Mathematics, Artificial Intelligence, or a related discipline. Specialized coursework or research in 3D deep learning, reinforcement learning, and advanced algorithmic design. Strong mathematical foundation.

Tools/Skillset if applicable

Expertise in deep learning and machine learning algorithms, particularly in the context of 3D vision, reinforcement learning, and generative geometric models. Strong proficiency in programming languages like Python and C++. Familiarity with ML frameworks (PyTorch, TensorFlow) and specialized 3D/geometric processing libraries (e.g., PyTorch3D, Open3D, Trimesh, or similar). Exceptional problem-solving skills with a focus on building algorithmic solutions from the ground up. Familiarity with cloud-based solutions and tools (AWS, GCP, or Azure) for scalable model training and distributed computing.

Domain Knowledge

• Proven experience developing state-of-the-art Deep Learning models, specifically focusing on Computer Vision, 3D CNNs, and Geometric Deep Learning.
• Strong expertise in Deep Reinforcement Learning (DRL) and developing autonomous agents for complex decision-making tasks in spatial, geometric, or simulated environments.
• Hands-on experience working extensively with 3D data representations, including 3D meshes, point clouds, voxels, and CAD data structures (e.g., B-rep, STEP, IGES).
• Deep knowledge of core mathematical algorithms, computational geometry, linear algebra, and numerical methods, with a proven ability to implement complex algorithms from scratch.
• Practical knowledge of deploying custom AI models and algorithms at scale in production environments.

Job Description

• Research & Development: Lead and contribute to research initiatives focused on advanced deep learning models, particularly 3D Convolutional Neural Networks (CNNs), Geometric Deep Learning, and Deep Reinforcement Learning (DRL) for spatial problem-solving.
• Model Design & Implementation: Design and implement custom neural network architectures and autonomous agents capable of interacting with, interpreting, and generating complex 3D geometries, including CAD data and 3D meshes.
• Mathematical & Algorithmic Engineering: Develop, optimize, and implement core mathematical algorithms from first principles. Heavily utilize computational geometry, topology, and linear algebra to process spatial data natively, building proprietary solutions rather than relying on off-the-shelf commercial software.
• Training & Fine-Tuning: Conduct training and fine-tuning of complex models (e.g., DRL agents, 3D CNNs) in simulated and geometric environments, leveraging modern machine learning frameworks such as PyTorch and TensorFlow.
• Optimization: Work on scaling, optimizing, and improving the efficiency of custom algorithms and large models, including distributed training, parallelism, and hardware acceleration (GPUs, TPUs).
• Deployment & Integration: Collaborate with engineering teams to integrate these 3D deep learning models and agentic workflows into robust, scalable production systems.