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Create compelling public-facing films, stills, social content, and interactive 3D prototypes showcasing SpAItial’s world models. Build Three.js experiences including Gaussian splat scenes, AR, and XR; direct, shoot, edit, composite, grade, and add VFX to visual artefacts. Own the company’s social presence, audience growth, creative community programs, and partner initiatives while collaborating closely with product engineers.

The Research Engineer will develop innovative generative 3D machine learning methods, train and optimize models, and refine 3D content generation processes, focusing on geometric accuracy and integrating physics-aware capabilities.

As a 3D Graphics & Rendering Engineer, you'll design and optimize synthetic datasets for 3D models, enhance rendering pipelines, and collaborate with research teams.

Design and develop image/video diffusion models, perform large-scale model training, maintain data for training, and optimize models for production use.

Own recurring business operations, including finance cycles, payroll, onboarding, compliance, vendors, workplace management, process documentation, and reporting. Partner with the COO, accountants, advisors, employees, suppliers, and German colleagues to maintain accurate finances, reliable workflows, regulatory compliance, and effective office operations at a fast-moving AI startup.

Own end-to-end training of vision-language-action and world-action robot policies, from dataset curation and model architecture through distributed training, evaluation, and deployment. Responsibilities include adapting VLM backbones for control, designing action representations, reducing the sim-to-real gap, building action-conditioned world models, and applying supervised fine-tuning and reinforcement learning. The role also contributes to technical direction for embodied AI research.

Research Scientist developing robust 3D reconstruction systems using multi-view geometry, SfM, SLAM, bundle adjustment, deep multi-view stereo, learned matching, and monocular depth. Responsibilities include designing pose estimators and reconstructors, scaling methods to real-world datasets, evaluating accuracy and quality, integrating state-of-the-art models, and collaborating on production deployment. Requires advanced computer vision research expertise, Python and PyTorch proficiency, and practical experience with COLMAP, ORB-SLAM, and Ceres.

The role involves building and maintaining ML and cloud infrastructure for training large-scale generative models. Responsibilities include designing GPU clusters, supporting distributed training, managing storage systems, and collaborating with researchers and engineers.

The role involves building and operating ML systems and infrastructure for large foundation models, data pipelines, and experiment orchestration in a collaborative environment.

Lead research on designing, building, training, and optimizing diffusion-based generative models for high-quality 3D content from diverse inputs, emphasizing world-scale scenes.