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

About CONXAI

CONXAI has built an
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