Staff AI/ML Architect, Embodied AI

Primary Function of Position

We are building advanced augmented dexterity capabilities for next-generation robotic platforms. As a Staff AI/ML Architect, you will own the end-to-end architecture of our applied-AI system: a hierarchical, multimodal stack in which a high-level model interprets sensory observations and produces structured intent, and a low-level policy turns that intent into precise, safe, real-time control. You will set the technical direction the rest of the AI/ML team builds against, define the interfaces between perception, reasoning, and control, and make the architecture decisions that let us move safely from offline research to real-time deployment.

Roles And Responsibilities

• Define and own the end-to-end architecture for a hierarchical perception, reasoning, and control AI system.
• Specify the contracts between layers: model outputs, policy interfaces, timing budgets, and safety hooks, and keep them stable as components are swapped and upgraded.
• Make the build and modular-vs-monolithic calls, evaluate in-house approaches against the state of the art and set the target architecture.
• Establish the path from offline evaluation on recorded data to real-time integration, including the continuous-improvement (human-in-the-loop) data loop.
• Partner across research, engineering, data, and product teams, mentor senior scientists/engineers and raise the engineering bar across the effort.
• Partner with AI/ML researchers, robotics, data engineers, and other stakeholders to deliver a modular architecture that enables rapid prototyping and learning while working toward a product solution.

Qualifications

• MS or PhD in CS, EE, Robotics, or a related field with 10+ years building AI/ML models and algorithms for autonomous-systems software, with demonstrated ownership of an end-to-end AI/ML system architecture taken to production or to a rigorous prototype.
• Deep experience with hierarchical, modular AI/ML or robot-learning stacks, including high-level reasoning paired with low-level control policies.
• Strong grasp of real-time and safety-critical ML: latency budgets, failure modes, fallback/abort behavior, and interface/contract design across components.
• Hands-on expertise with modern deep learning and multimodal, vision-language, LLM-based architectures, including vision foundation models (VFM), vision-action (VA), and vision-language-action (VLA) models.
• Fluency in one of PyTorch, TensorFlow, JAX, and in Python and C++, is required.
• Working knowledge of world-model and self-supervised predictive architectures (e.g., JEPA-style models, MAE, DINO) and how learned world models inform perception-and-control design.
• Solid foundations in linear algebra, probability, and optimization, enough to reason about and debug model behavior from first principles.
• Proven cross-functional technical leadership (able to align research, engineering, and product stakeholders around an architecture).

Preferred Qualifications

• Background in healthcare, medical devices, surgical robotics, or other regulated technical domains.
• Foundation-model adaptation and fine-tuning for embodied robotics tasks.
• Experience delivering AI/ML in a real-time, safety-critical domain.
• Imitation learning, DAgger, and/or reinforcement learning at system scale.
• Sim-to-real workflows and experience with robotics simulators (e.g., NVIDIA Isaac Sim).
• Regulatory-aware AI/ML development for regulated, safety-critical industries.
• Triton kernel and/or CUDA development experiences.
• Publications or recognized contributions at venues such as CVPR, NeurIPS, CoRL, RSS, or ICRA.
• Awareness of data governance in regulated environments (HIPAA, FDA).