Physical AI Automation Engineer / Senior Engineer

Accenture•St. Louis, MO

1d•$26 - $99•Hybrid

About The Position

The Physical AI Automation Engineer is a hands-on practitioner who develops and integrates digital twin, 3D, simulation and industrial-data solutions that connect virtual environments with physical operations. The role has broad Physical AI scope, with primary depth in digital twin and simulation engineering, ecosystem integration and operational use of virtual models. Robotics training and orchestration experience is valuable but complementary rather than the defining center of the role.

Requirements

Minimum 3 years of experience in digital twins, 3D engineering, simulation, industrial software, game engines, engineering applications or systems integration.
Hands-on experience in at least one core area: 3D model development and integration; discrete-event or process simulation; physics-based simulation; virtual commissioning; industrial data integration.
Experience with one or more relevant tools or ecosystems such as NVIDIA Omniverse, OpenUSD, Unity, Unreal Engine, Siemens, Dassault Systemes, PTC, Ansys, FlexSim, AnyLogic, Visual Components or comparable platforms.
Programming or scripting capability in Python, C++, C# or a comparable language.
Ability to translate engineering or operational requirements into models, simulation scenarios, integrations and testable outcomes.
Understanding of software engineering, testing, documentation and structured delivery practices.

Nice To Haves

NVIDIA Omniverse, OpenUSD, Nucleus, Kit SDK, Isaac Sim or related NVIDIA technologies.
CAD/BIM/PLM integration, 3D optimization, asset conversion, reality capture, scanning or spatial-data pipelines.
Discrete-event, process-flow, physics, controls or virtual-commissioning experience.
Industrial ecosystem integration across IoT, historians, PLC/SCADA, MES, WMS, ERP and cloud or edge platforms.
Robotics simulation, synthetic data, robot training, reinforcement or imitation learning, simulation-to-real transfer, fleet orchestration.
Experience applying these capabilities in manufacturing, warehousing, logistics, engineering, capital projects, laboratories, data centers or other asset-intensive environments.
Experience working with client engineering and operations teams in consulting, systems integration or product delivery.

Responsibilities

Develop and integrate digital twins for products, equipment, production lines, factories, warehouses, engineering environments and other physical operations.
Create and prepare 3D environments and asset pipelines using CAD/BIM/PLM inputs, 3D models, scans or reality-capture data, with appropriate asset hierarchy, semantics and configuration.
Build solutions using NVIDIA Omniverse, OpenUSD, Nucleus, Kit-based applications or comparable 3D and virtual-world ecosystems.
Develop or integrate discrete-event, process-flow, physics-based and virtual commissioning simulations for layout, throughput, material flow, cycle time, resource utilization and operational validation.
Connect digital twins and simulations with industrial and enterprise systems such as IoT platforms, historians, PLC/SCADA, MES, WMS, ERP, PLM and engineering data.
Develop Python or C++ extensions, connectors, APIs, data pipelines and automation services that connect models, simulation engines, operational data and downstream applications.
Support model calibration, scenario design, simulation experiments, validation, sensitivity analysis and interpretation of results with engineering and operations stakeholders.
Create virtual environments for design reviews, operator training, process validation, commissioning preparation and operational decision support.
Implement testing, version control, configuration management, CI/CD, deployment, monitoring and documentation practices for production-quality twin and simulation solutions.
Collaborate with architects, manufacturing and warehouse SMEs, simulation specialists, data/AI teams, robotics teams and ecosystem partners.
Contribute reusable digital-twin templates, connectors, simulation components, reference implementations and delivery standards.