Head of Spatial AI

Bits Body develops next-generation computational modeling & simulation technologies to optimize engineering design & analysis workflows for spatial AI applications.

We’re seeking an entrepreneurial, high-ownership individual contributor to drive the core engineering, development, and deployment of our Generative AI platform, HumBoAI, an integral component of our medical technology solutions for anatomical modeling & simulation (M&S). This role is ideal for a hands-on direct execution operator with a proven ability to design, train, and product ionize multi-modal generative machine learning (ML) pipelines, build robust backend infrastructure & APIs, and develop an interactive 3D web interface that visualizes model outputs in real-time.

If you’re passionate about building a spatial AI company from the ground up, leveraging AI to solve critical problems, and have a track record of building and deploying complex AI systems, we want to hear from you!

Head of Spatial AI

San Jose, CA | Full-Time | Hybrid

• Foundational Spatial AI Engineering:
  Architect and optimize multi-modal, cross-domain end-to-end pipelines by training multi-scale vision and spatial-temporal foundational models, integrating language-model conditioning where appropriate.
• API & Backend Infrastructure:
  Containerize models and expose robust, low-latency APIs for heavy generative inference workloads.
• Production Deployment:
  Productionize models as cloud services; implement model versioning, CI/CD, automated testing, and cloud-native deployment pipelines.
• Model Lifecycle Management:
  Implement post-deployment monitoring, drift detection, A/B testing, and automated retraining workflows to maintain performance and safety.
• 3D Modeling & Simulation:
  Implement and validate geometry pipelines (meshes, SDFs, implicit fields), ensure simulation-readiness, and integrate downstream finite element analysis (FEA) workflows.
• Full-Stack 3D Platform Development:
  Architect and build a responsive, high-performance 3D web frontend and interactive workspace that visualizes real-time ML computations.
• Integrated Tooling:
  Embed volumetric viewers and mesh editors with analysis toolsets for both medical images and polygonal meshes.
• Strategic Alignment:
  Collaborate closely with the technical team to verify system-level behavior and translate core anatomical M&S research into stable, production-ready enterprise applications.
• Post-Funding Operations:
  Build early engineering frameworks and establish the architectural blueprint to recruit and lead the core spatial AI engineering team once institutional funding scales.

• Education & Experience:
  
  A Bachelor’s degree (B.S./B.E./B.Tech) in Computer Science, Data Science, Machine Learning, Computer Vision, or a related quantitative or scientific field with at least 10 years of progressive experience; OR a master’s degree (M.S./M.E./M.Tech) in one of these fields with at least 8 years of relevant experience; OR a doctoral degree (Ph.D.) in one of these fields with at least 4 years of relevant experience, is required, demonstrating hands‑on development of data-intensive web apps involving spatial AI & production-grade deployment capability in the required areas listed below.
• Multi-Modal & Geometric ML Modeling:
  Experience building & training probabilistic generative models in 3D computer vision and spatial geometry (normalizing flows, diffusion models, VAEs, NeRF/implicit fields, and Gaussian Splatting) alongside natural language processing architectures (transformers, BERT, and T5) for handling image-to-3D or text-to-3D neural, generative pipelines.
• Core Technical Stack:
  Expert-level command of high-performance computing using the following:
  • Backend/AI (Python for 3D ML) - Proficiency in training graph neural networks and geometric deep learning systems using PyTorch ecosystem (including PyTorch3D, PyTorch Geometric, and Deep Graph Library), MONAI for medical imaging workflows, and familiarity with Tensor Flow ecosystem where applicable (including TensorFlow3D, Tensor Flow Graphics, Tensor Flow GNN, and Graph Net).
  • Frontend/Graphics…