Applied AI Engineer Spatial and Embodied AI

What you'll work on

Applied AI/ML Engineering & Mission Ownership

• Embed with partners to solve the problems with our frontier AI/ML tools, informing our research and product development plan along the way…not just deploy software.
• Be the primary filter between the messy reality of the physical world and our research and technical staff, surfacing real commercial challenges and pain-points.
• Build and tune models, prototype, script, and patch (often in the field), turning ambiguous requirements into executable code.

Systems Integration & Edge Compute

• Build the connective tissue between our AI and the customer's reality, and then help them rethink the art of the possible. This means writing high-performance code (C++/Go/Python) that integrates our inference engine with legacy sensors, RTOS, and diverse hardware peripherals.
• Optimize complex ML models for survival in harsh computing environments.
• Leverage our simulation and world-model capabilities to validate operational plans before they touch physical hardware.

Technical Diplomacy

• Translate the probabilistic nature of AI into the deterministic language of industrial control systems and mission operators.
• Explain trade-offs to non-technical individuals and deep technical details to systems engineers, building the trust required to deploy autonomous systems in critical paths.

What we're looking for

Required

• 5+ years experience taking complex systems from prototype to production, within software engineering or applied AI/ML
• Strong experience in the ML stack (Python, Docker, Kubernetes, infrastructure-as-code, and CI/CD for ML pipelines) with competent systems programming skills (C++, Go, Rust, or Java), and ability to use modern AI coding tools
• Strong applied machine learning experience, specifically in the lifecycle of deploying, evaluating, and debugging models

• Experience in at least one of the following, with working knowledge of the others:

  • Agents or policy learning (e.g., RL, planning, control theory, spatial reasoning)
  • World models, simulation environments (Unity/Unreal, Omniverse, Isaac Sim), or model-based learning
  • Perception, sensor fusion, or inverse dynamics models (IDMs)

• Exposure to bridging the "hardware-software" gap: integrating AI inference with sensors, edge devices, RTOS, or legacy industrial networks

• Full-stack systems mindset: understanding of memory management, concurrency, networking, and APIs

• U.S. citizenship and ability to obtain and maintain a national security clearance (TS/SCI preferred)

• Ability to comply with export control requirements (ITAR/EAR)

• Experience, and comfort in, forward-type environments often found with partners across the industrial base, defense, intelligence, aerospace, and robotics environments at the edge
• Edge AI, inference optimization, or deployment in constrained settings (TensorRT, ONNX, or mobile inference as examples)
• Background in autonomous systems, control, or real-time systems
  Startup or early-stage engineering experience
• Understanding of secure systems engineering or DevSecOps experience in regulated industries, including degraded, intermittent, limited) networking constraints
• Open-source contributions or demonstrable applied systems work, or a portfolio of "side projects" that demonstrate AI/ML, and engineering curiosity