Applied AI Engineer (Spatial and Embodied AI)

Role overview

Responsibilities

• check_circle 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.
• check_circle 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.
• check_circle Build and tune models, prototype, script, and patch (often in the field), turning ambiguous requirements into executable code.
• check_circle 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.
• check_circle Optimize complex ML models for survival in harsh computing environments.
• check_circle Leverage our simulation and world-model capabilities to validate operational plans before they touch physical hardware.
• check_circle Translate the probabilistic nature of AI into the deterministic language of industrial control systems and mission operators.
• check_circle 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.
• check_circle 5+ years experience taking complex systems from prototype to production, within software engineering or applied AI/ML
• check_circle 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
• check_circle Strong applied machine learning experience, specifically in the lifecycle of deploying, evaluating, and debugging models
• check_circle 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)
• check_circle Agents or policy learning (e.g., RL, planning, control theory, spatial reasoning)
• check_circle World models, simulation environments (Unity/Unreal, Omniverse, Isaac Sim), or model-based learning
• check_circle Perception, sensor fusion, or inverse dynamics models (IDMs)
• check_circle Exposure to bridging the "hardware-software" gap: integrating AI inference with sensors, edge devices, RTOS, or legacy industrial networks
• check_circle Full-stack systems mindset: understanding of memory management, concurrency, networking, and APIs
• check_circle U.S. citizenship and ability to obtain and maintain a national security clearance (TS/SCI preferred)
• check_circle Ability to comply with export control requirements (ITAR/EAR)
• check_circle Experience, and comfort in, forward-type environments often found with partners across the industrial base, defense, intelligence, aerospace, and robotics environments at the edge
• check_circle Edge AI, inference optimization, or deployment in constrained settings (TensorRT, ONNX, or mobile inference as examples)
• check_circle Background in autonomous systems, control, or real-time systems Startup or early-stage engineering experience
• check_circle Understanding of secure systems engineering or DevSecOps experience in regulated industries, including degraded, intermittent, limited) networking constraints
• check_circle Open-source contributions or demonstrable applied systems work, or a portfolio of "side projects" that demonstrate AI/ML, and engineering curiosity
• check_circle ML & Research: Python, PyTorch, NumPy, OpenCV, Triton, CUDA for large-scale training, real-time inference, and applied CV/ML
• check_circle Pipelines & Experimentation: Kubeflow Pipelines and Airflow with continuous evaluation, A/B testing, and performance monitoring across training and production
• check_circle Backend & Systems: Java services with Redis and RabbitMQ, plus performance-critical C++ components; containerized with Docker and Kubernetes on GCP or on-prem
• check_circle Clients & Edge Software: Electron/React desktop apps, C# and C++ high-performance recorders, and mobile clients in Swift (iOS) and Kotlin (Android)
• check_circle Infra, Hardware & Other Deployments: Terraform-managed infrastructure, CI/CD via GitHub Actions and CircleCI; deployment to NVIDIA GPU clusters, air-gapped or on-prem environments, hardened Linux systems (FIPS/STIG), and constrained real-world hardware requiring model optimization, hardware-specific acceleration, and secure supply-chain practices

Benefits

• check_circle Competitive salary and meaningful equity
• check_circle Comprehensive health insurance including dental and vision insurance
• check_circle 401k