3D Reconstruction Specialist

At World Labs were building Large World ModelsAI systems that understand reason about and interact with the physical world. Our work sits at the frontier of spatial intelligence robotics and multimodal AI with the goal of enabling machines to perceive and operate in complex real-world environments.

Were assembling a global team of researchers engineers and builders to push beyond todays limitations in artificial intelligence. If youre excited to work on foundational technology that will redefine how machines understand the worldand how people interact with AIthis role is for you.

About World Labs:

World Labs is an AI research and development company focused on creating spatially intelligent systems that can model reason and act in the real world. We believe the next generation of AI will not live only in text or pixels but in three-dimensional dynamic environmentsand we are building the core models to make that possible.

Our team brings together expertise across machine learning robotics computer vision simulation and systems engineering. We operate with the urgency of a startup and the ambition of a research lab tackling long-horizon problems that demand creativity rigor and resilience.

Everything we do is in service of building the most capable world models possibleand using them to empower people industries and society.

Role Overview

Were looking for a 3D Reconstruction Specialist to develop and advance state-of-the-art methods for reconstructing high-quality 3D geometry and appearance from real-world data. This role is focused on modern reconstruction techniquesboth feed-forward and optimization-basedwith an emphasis on novel representations robust optimization and scalable training and inference pipelines.

This is a hands-on research-driven role for someone who enjoys working at the intersection of computer vision graphics and machine learning. Youll collaborate closely with research scientists ML engineers and product teams to translate cutting-edge reconstruction ideas into production-ready systems that power core product capabilities.

What You Will Do:

• Design and implement modern 3D reconstruction systems including feed-forward and optimization-based approaches for geometry appearance and scene understanding.
• Research prototype and productionize advanced 3D representations (e.g. implicit functions point-based or volumetric methods hybrid representations) with a focus on accuracy efficiency and scalability.
• Develop and improve optimization pipelines for multi-view reconstruction including camera pose estimation joint geometry/appearance optimization and robust loss formulations.
• Build end-to-end training and evaluation workflows for 3D reconstruction models from data preparation and supervision strategies to large-scale experiments and metrics.
• Collaborate with data and infrastructure teams to ensure reconstruction methods integrate cleanly with existing 3D data pipelines rendering systems and downstream applications.
• Analyze failure modes and data quality issues in real-world reconstruction scenarios and design principled solutions to improve robustness and generalization.
• Optimize performance across the stack including memory usage training speed and inference latency to support large-scale datasets and production constraints.
• Contribute to technical direction by proposing new research ideas mentoring teammates and helping set best practices for 3D reconstruction across the organization.

Key Qualifications:

• 6 years of experience working on 3D reconstruction multi-view geometry or related areas in computer vision graphics or machine learning.
• Strong foundation in modern 3D reconstruction techniques including feed-forward neural methods or optimization-based approaches.
• Deep experience with 3D representations and their tradeoffs (e.g. implicit fields point-based methods meshes volumes) or with large-scale optimization pipelines for reconstruction.
• Proficiency in Python and/or C with hands-on experience building research or production systems.
• Experience with deep learning frameworks (e.g. PyTorch) and numerical optimization tools.
• Familiarity with rendering differentiable rendering or graphics pipelines and how they interact with reconstruction systems.
• Proven ability to work in ambiguous fast-moving environments and drive projects from concept through deployment.
• A strong sense of ownership and scientific rigor: you care deeply about correctness reproducibility and measurable improvements.
• Enjoy collaborating with a small high-caliber team and raising the technical bar through thoughtful design experimentation and code quality.

Who You Are:

• Fearless Innovator: We need people who thrive on challenges and arent afraid to tackle the impossible.
• Resilient Builder: Impacting Large World Models isnt a sprint; its a marathon with hurdles. Were looking for builders who can weather the storms of groundbreaking research and come out stronger.
• Mission-Driven Mindset: Everything we do is in service of creating the best spatially intelligent AI systems and using them to empower people.
• Collaborative Spirit: Were building something bigger than any one person. We need team players who can harness the power of collective intelligence.

Were hiring the brightest minds from around the globe to bring diverse perspectives to our cutting-edge work. If youre ready to work on technology that will reshape how machines perceive and interact with the world World Labs is your launchpad.

Join us and lets make history together.

Equal Opportunity & Pay Transparency

Equal Employment Opportunity

World Labs is an equal opportunity employer. We do not discriminate on the basis of race color religion sex sexual orientation gender identity national origin age disability genetic information veteran status or any other characteristic protected under applicable law. We welcome all qualified applicants and are committed to providing reasonable accommodations throughout the hiring process upon request.

California Pay Transparency

In accordance with California law we disclose the following:

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