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

About the role

**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