Sr. Computer Vision Engineer (3D Semantic Scene Understanding)

Job description

About CONXAI CONXAI has built a no-code, agentic AI platform for the Architecture, Engineering and Construction (AEC) and physical industries, focused on knowledge-automation . We automate high-stakes, knowledge-intensive workflows traditionally trapped in siloed data, fragmented tools and tacit (undocumented) human expertise. Our multi-agent systems perform complex reasoning in the physical world; and transform bespoke, service-heavy processes into scalable Service-as-a-Software automation. CONXAI is trusted by some of the leading AEC companies in Europe, US, LATAM and Japan. Your Role As a Senior ML Engineer, you will lead the development of the spatial reasoning engine for our agentic AI platform. Your work focuses on the intersection of 3D Semantic Reconstruction , Geometric Deep Learning , and Agentic Inference . You will be responsible for building pipelines that transform unstructured multi-modal data into structured, actionable Spatial Knowledge Graphs . You will prioritize topological accuracy and semantic grounding , over photorealistic neural rendering. You will design the logic that allows autonomous agents to navigate, reason about, and perform inference on complex 3D environments, ensuring that AI-driven insights are rooted in the physical and engineering constraints of the real world. What You’ll Do Semantic Scene Reconstruction: Develop algorithms for 3D scene representation that prioritize geometric primitives and semantic labels over pixel-accuracy. This includes surface reconstruction, occupancy mapping and volumetric segmentation Multi-Modal Fusion: Architect systems that fuse panoptic segmentation representations from CONXAI’s AEC Foundation model with 3D models to generate high-fidelity, labeled representations Knowledge Graph Augmentation: Automate the augmentation of 3D spatial data to CONXAI’s Spatio-Temporal Knowledge Graphs , from reconstructed 3D scenes, mapping the hierarchical and functional relationships between structural elements Agentic Inference & Reasoning: Design agentic workflows that perform complex reasoning tasks directly on the STKG Actionable Affordance Mapping: Implement methods to identify "affordances" within a 3D volume, defining how agents or users can interact with the environment based on its physical geometry and engineering logic Optimization & Scaling: Deploy SOTA models, representations and inferred domain context into production use-cases that deliver significant value to customers What We’re Looking For MS / PhD in Computer Science, Robotics, Electrical Engineering or related field 3+ years of industry experience in Computer Vision and Deep Learning 2+ years of leading 3D Computer Vision projects, specifically, geometric deep learning, 3D reconstruction Experience with physics engines, e.g., NVIDIA Isaac Gym, MuJoCo, PyBullet, etc. is a plus Experience in Agentic AI implementations with GraphRAG, Langgraph/LlamaIndex is a plus Exceptional implementation experience with Open3D / PyTorch 3D, reconstruction (multi-view stereo, surface reconstruction and mesh-fitting, e.g., with TSDF), 2D → 3D “lifting” Thorough understanding of software design Previous experience in a fast-paced technology startup environment is a plus Fluent and articulate in English Why CONXAI Edge of Innovation: Be at the absolute forefront of AI in the construction tech space High Autonomy: Contribute to a new paradigm for multi-modal scene understanding and reasoning - owning the logic, performance, and customer impact Top-Tier Peer Group: Work with a global team of ML engineers, software engineers and industry practitioners Equity & Scale: Competitive compensation with significant equity upside