[Jobs] Computer Vision & 3D Perception Engineer

[Heni Ben Amor]

At PerceptAI, we are building the Foundation Model for Physical AI and Spatial Intelligence in the real world. Our mission is to develop a breakthrough, multi-modal understanding of the physical environment, empowering both humans and machines to intelligently perceive, reason about, and interact with the space around them. By turning complex real-world data into high-fidelity, actionable digital twins, we are unlocking the next generation of spatial awareness for critical industries, including urban operations, robotics, emergency response, and beyond: https://www.usepercept.ai

As a Computer Vision & 3D Perception Researcher/Engineer, you will be at the forefront of core architecture development. You will design, train, and scale deep learning models that synthesize dense 3D environments from vast, heterogeneous datasets. Leveraging cutting-edge computer vision techniques such as Gaussian Splatting and Semantic Segmentation, you will develop the foundational systems that allow PerceptAI to reconstruct and comprehend the real world at an unprecedented scale. In particular, you will design and pioneer deep learning architectures optimized for 3D perception, bridging the gap between raw visual inputs and structured, scenegraph representations of the world.

Responsibilities

• Scale 3D Reconstruction: Build and optimize high-throughput data pipelines to train models on massive, real-world datasets, ensuring efficient distributed training across large GPU clusters.
• Advance Gaussian Splatting and Scenegraph Extraction: Implement and advance state-of-the-art 3D modeling frameworks to achieve real-time, photo-realistic, and geometrically accurate scene synthesis.
• Cross-Domain Deployment: Adapt 3D perception models to power mission-critical application domains, ensuring robust, real-world performance for robotics, autonomous urban operations, and emergency response.
• Collaborative Innovation: Work closely with engineering and product leads to translate cutting-edge research into high-performance, production-grade APIs and software toolkits.

Required Qualifications

• Education: Master’s or PhD in Computer Science, Electrical Engineering, Robotics, or a related field with a heavy emphasis on Computer Vision and Deep Learning (or equivalent industry experience).
• Deep Learning Mastery: Expert-level fluency in standard deep learning frameworks (PyTorch, JAX) and a proven track record of designing, training, and troubleshooting complex neural network architectures.
• Large-Scale Engineering: Hands-on experience working with massive, unstructured datasets and managing large-scale distributed training infrastructure.
• 3D Perception Depth: Strong foundational knowledge of 3D computer vision concepts (e.g., multi-view geometry, camera calibration, SfM, or SLAM).
• Implementation Excellence: Ability to write clean, maintainable, production-quality research and deployment code (Python/C++), with a strong focus on algorithmic efficiency.

Pluses (Nice to Have)

• Direct experience modifying, scaling, or deploying 3D Gaussian Splatting for large-scale scene representation.
• Experience with feed-forward reconstruction models and approaches
• Experience with scenegraphs and other multi-level 3D representations of environments and objects. 
• A strong publication record at machine learning and computer vision venues (CVPR, ICCV, ECCV, NeurIPS, ICLR, SIGGRAPH).

What We Offer

• Highly competitive base salary.
• Substantial equity/stock options, allowing you to deeply share in the upside of the foundation model company you are helping build.
• Comprehensive health, dental, and vision benefits.
• The opportunity to work on highly impactful, real-world problems that reshape emergency response, robotics, and urban infrastructure.

To Apply: Join us as we bridge the gap between advanced deep learning and 3D geometry to build the ultimate spatial foundation model. To apply, please send your resume + an optional 2 page portfolio document with GitHub/Google Scholar profile to: jo...@usepercept.ai