Head of Spatial AI

BitsBody; San Jose (CA); Hybrid; USD 180,000 - 260,000; Full time

Benefits offered by this job

Health, dental, vision

Retirement plan

Paid family leave

Job summary

BitsBody seeks a hands-on leader to drive the core engineering, development, and deployment of HumBoAI for anatomical modeling & simulation.

You will design, train, and productionize multi-modal generative ML pipelines, build robust backend APIs, and develop a real-time 3D web interface for visualizing model outputs.

Qualifications

• Advanced degree with 8+ years in ML/CV and production-grade web apps.
• Experience building multi-modal 3D ML pipelines and spatial AI systems.
• Strong backend/frontend stack with Python, PyTorch3D, Three.js and APIs.

Responsibilities

• Architect and optimize multi-modal end-to-end pipelines for spatial AI.
• Containerize models and expose low-latency APIs for inference.
• Productionize models as cloud services with CI/CD and testing.
• Model lifecycle: monitoring, drift detection, retraining workflows.
• Develop a high-performance 3D web frontend for real-time visualization.
• Integrate medical imaging workflows and 3D mesh tooling.

Skills

Spatial AI

Multi-modal ML

Python

3D Visualization

Frontend JS

APIs

DevOps

Kubernetes

HIPAA awareness

Education

Bachelor's degree in CS/DS/ML

Master's or PhD in related field

Tools

PyTorch3D

PyTorch Geometric

Three.js

React Three Fiber

Open3D

Job description

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

The Opportunity

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 productionize 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!

Primary Title

Head of Spatial AI

Location & Work Type

San Jose, CA | Full-Time | Hybrid

Roles & Responsibilities

• 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.

Skills & Qualifications

• 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 TensorFlow ecosystem where applicable (including TensorFlow3D, TensorFlow Graphics, TensorFlow GNN, and Graph Net).
  • Frontend/Graphics (JavaScript/TypeScript for modern, high-throughput 3D browser rendering) - Proficiency in real-time streaming of large volumes arrays & dense geometric meshes, including Three.js/React Three Fiber/Babylon.js, WebAssembly (Wasm), and WebGL/WebGPU.
• Backend & APIs: Proven experience building robust APIs with FastAPI, GraphQL, gRPC/ Protobuf (for efficient large 3D array transfers), and streaming protocols (WebSockets/WebRTC) to push progressive updates to the client; familiarity with Celery, Redis, PostgreSQL, and object storage for large 3D arrays.
• Production DevOps: Deep experience containerizing heavy GPU workloads using Docker and orchestrating scalable deployments with Kubernetes and Helm on AWS or GCP (EKS/GKE, S3/Cloud Storage, IAM).
• Model Serving & Inference: Experience with NVIDIA Triton, ONNX Runtime, TorchServe, or custom FastAPI/gRPC inference stacks; knowledge of model quantization, graph pruning, and inference optimization.
• Performance Engineering: GPU programming familiarity (CUDA, cuDNN), distributed training (NCCL), and profiling/optimization for large models and volumetric data.
• 3D Tooling & Data Libraries: Proficient with medical imaging frameworks (PyDicom, SimpleITK/ITK, NiBabel for DICOM/NIfTI preprocessing and segmentation) and 3D mesh/point cloud manipulation libraries (Open3D, Trimesh, MeshLib, Kaolin or equivalent).
• Security & Compliance Awareness: Practical understanding of PHI handling, DICOM security considerations, and HIPAA-aware engineering practices.
• Execution Bias: Ability to operate autonomously in ambiguity and build structure from absolute zero.

Preferred

• Startup Exposure: Prior experience within the early-stage startup lifecycle (founder or early employee as a technical leader in Pre-seed through Series A), demonstrating resilience & adaptability in a steep learning-focused environment.
• C++ Kernels: Experience compiling and optimizing C++ kernels for performance-critical pipelines – medical image volumes (e.g., ITK, VTK) and geometry (e.g., OpenCASCADE, Manifold3D).
• Biomechanical Solvers: Experience with medical imaging pipelines (CT/MRI preprocessing, segmentation, registration), physics-based simulation engines (OpenSim, FEA integrations), and geometry/CAE toolchains (OpenCASCADE, VTK).
• Research Dissemination: Strong peer-reviewed publications or open‑source contributions in ML, computer vision, or 3D geometry.

Equity & Compensation

All equity allocations or stock options are subject to a standard 4-year vesting schedule with a 1-year cliff, matching institutional venture practices. We’re offering two (2) distinct pathways for this role.

• Option #1 (Pre-funding): Join the team as Co-founder & Head of Spatial AI, before we raise our first round of outside capital. This is initially an equity-only role, offering a meaningful co-founder-level equity stake. Upon securing outside funding, there is a clear path to a balanced salary & benefits package.
• Option #2 (Post-funding): Join the team as Head of Spatial AI, after we raise our first round of outside capital. Compensation will include a competitive salary & benefits package. Any stock options will be commensurate with a senior-level hire. Candidates selecting Option #2 will be considered only if no qualified candidate is found for Option #1.

Benefits & Culture Highlights

• Mission-driven, high-velocity, and high-ownership environment.
• Direct influence on product, architecture, and long-term direction.
• Opportunity to build a category-defining technology from the ground up.
• Culture of transparency, intellectual rigor, and responsible innovation.
• Collaborative environment bridging medical, engineering, science, and commercialization.
• Post-funding benefits will include health, dental, vision, retirement, and paid family leave.

Contingency Disclaimer

This position is contingent upon a successful background check. Role scope may evolve based on the company’s growth & funding status. Any compensation pathway besides equity or stock options is contingent upon securing outside funding.

No Third-Party Contractors

We do not accept resumes, inquiries, or outreach from third-party recruiters, staffing agencies, or talent consultants.

U.S. Work Authorization

Applicants must possess valid, permanent authorization to work for any employer in the United States. We’re unable to sponsor or assume sponsorship of employment visas (e.g., H-1B) at this time.

Skills: dicom,computer vision,webassembly,python,devops,spatial modeling,fastapi,typescript,react