Physical AI Engineer

Design, deploy, and validate Physical AI systems that bring machine learning, computer vision, and sensing intelligence into robotics for manufacturing.

Responsibilities

• Lead deployment and integration of advanced AI models into robotic and sensing systems (machine learning, computer vision, generative AI and agents, reinforcement learning) to improve decision-making and task execution in real-world manufacturing.
• Integrate AI with robotic platforms and sensing inputs including cameras, AGVs, co-bots, drones, and quadruped robots for adaptable behavior.
• Design, build, and operate an edge-to-cloud reference architecture covering sensor, edge inference, connectivity, data, intelligence, and experience.
  • Run GPU edge inference on site.
  • Train models in the cloud on AWS.
  • Ingest data through OT/IT integration layers (e.g., Ignition, MQTT, AWS IoT Greengrass, Aveva PI, Azure IoT Hub).
  • Land data in the Lilly Medicine Foundry data platform.
  • Use orchestration plus an ontology spanning all layers.
• Evaluate build, buy, and hybrid options for edge hardware and edge-to-cloud software components.
• Collaborate with engineers and data scientists to optimize AI algorithms and model performance for real-time edge operation in robotics and vision applications, emphasizing minimal latency and robust performance across conditions.
• Manage the model lifecycle: training, versioning, performance monitoring, drift detection, and retraining.
• Build and maintain sim-ready asset libraries from CAD data and the equipment registry.
• Use simulation platforms (e.g., NVIDIA Omniverse, Isaac Sim) to progress from static to dynamic digital twins, generate synthetic data, and test robot/co-bot/AGV behavior and safety rules before deployment.
• Support twin-based use cases including lockout/tagout, SOP guidance, wayfinding to equipment, virtual tours, XR training, and smart P&ID and maintenance.
• Integrate vision and sensor outputs with manufacturing execution and quality systems so detections flow into an exception queue with an audit trail of Quality judgment on every AI flag (including cleaning verification and changeover checks).
• Expose equipment and twin data to AI agents (e.g., via MCP servers on OT data platforms) and generate incident summaries for HSE and Quality review.
• Develop novel verification methods to test and validate AI-driven robotics and vision performance in complex manufacturing environments.
• Design testing protocols and performance benchmarks that reflect AI-specific behaviors and incorporate simulation-based validation and execution of validation in line with GxP and Computer System Validation requirements, in partnership with Quality.
• Partner with safety experts to create and implement safety mitigations to meet industry standards and regulations for AI-driven systems in unstructured or dynamic manufacturing environments.
• Apply and verify safety rules for AGVs and co-bots, integrate with safety controllers, and support HSE use cases including lockout/tagout, near-miss analysis, and early incident detection with governance aligned to HSE and Quality.
• Ensure cybersecurity architecture review readiness, apply enterprise data governance and classification to image/video/sensor data, and complete AI intake and risk assessment before deployment.
• Oversee production deployment of robotics and vision systems, moving pilots from existing space and labs to the Foundry site as dependencies are met (site/instrument/camera connectivity and MES/ERP integration for AGVs), and provide troubleshooting, technical support, and optimization during integration.
• Work with technology and integration partners (e.g., NVIDIA, AWS, robotics and camera/drone vendors) to deliver partner-backed pilots, and evaluate which partner services are production-ready for edge vision near GMP areas.
• Bridge Digital, Robotics, Facilities, and Technology teams for pilot selection and sequencing based on value and dependencies.
• Maintain documentation for deployment processes, safety protocols, validation/testing results, and AI integration methods.
• Provide regular stakeholder reporting on system performance and improvements.

Requirements

• Bachelor’s degree in Robotics, Computer Science, Electrical Engineering, Mechatronics, or a related technical field.
• Minimum 5 years experience in robotics, automation, or AI-driven systems deployment in manufacturing or industrial settings.
• Experience deploying computer vision or other AI models at the edge (accelerated inference, video analytics pipelines) with models trained in the cloud, ideally in regulated or GMP environments.
• Experience with safety verification methods and risk analysis for autonomous or semi-autonomous systems, including AGVs and mobile robots (highly desirable).
• Strong programming skills in Python, C++, or ROS (Robot Operating System).
• Knowledge of AI frameworks (e.g., TensorFlow, PyTorch, OpenCV) and model optimization/serving tools (e.g., ONNX, TensorRT, Triton).
• Knowledge of industrial communication protocols (e.g., OPC-UA, MQTT, Modbus, Ethernet/IP).
• Experience with industrial controls systems and Safety Controllers.
• Experience with edge AI and GPU platforms (e.g., NVIDIA Jetson, DeepStream) and edge-to-cloud architectures for vision and sensor data.
• Experience with cloud services for IoT, data, and machine learning (e.g., AWS IoT Greengrass, SageMaker, S3, GPU compute) and familiarity with enterprise data platforms (e.g., Databricks, Microsoft Fabric).
• Familiarity with OT/IT data integration and historian platforms (e.g., Ignition, Aveva PI, HighByte) and manufacturing execution systems.
• Familiarity with simulation and digital twin tools (e.g., NVIDIA Omniverse, Isaac Sim, OpenUSD, CAD) and synthetic data generation.
• Familiarity with generative AI, LLMs, and agent frameworks (including MCP) applied to operations, quality, or safety use cases.
• Analytical and problem-solving mindset with ability to troubleshoot and optimize both hardware and software components.
• Familiarity with safety standards and risk mitigation strategies (e.g., ISO 10218, ISO 13482, ISO 3691-4, ISO 13849, EN 61508) and OT cybersecurity practices (e.g., IEC 62443).
• Excellent communication and teamwork skills, with ability to collaborate across IT, OT, Quality, HSE, and facilities and with external partners.
• Solid knowledge of Computer System Validation process and GxP requirements (e.g., GAMP 5, 21 CFR Part 11, EU Annex 11), including validation of AI-enabled systems.
• Experience in pharmaceutical or biologics manufacturing operations (e.g., changeover, cleaning verification, API processes) and integrating with quality and validation systems.
• Demonstrated ability to analyze, anticipate, and resolve complex technical, operational, or business issues; ability to prioritize and escalate effectively.
• Familiarity with data governance and AI risk-management practices for image, video, and sensor data.
• Demonstrated learning agility and curiosity.

Compensation

• Anticipated wage: USD 118,500 - 211,200 per year.

Benefits

• Company bonus (depending, in part, on company and individual performance).
• Eligibility to participate in a company-sponsored 401(k).
• Pension.
• Vacation benefits.
• Eligibility for medical, dental, vision, and prescription drug benefits.
• Flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts).
• Life insurance and death benefits.
• Certain time off and leave of absence benefits.
• Well-being benefits (e.g., employee assistance program, fitness benefits, employee clubs and activities).

Location and Work Model

• Indianapolis, IN (onsite).

Initial Pilots and Facility Context

• Initial pilots run in existing space and labs before moving to the Lilly Medicine Foundry site.
• Lilly Medicine Foundry (LMF) is a brand-new facility using the latest technology to augment the clinical supply chain for small molecules (SM), oligonucleotides, peptides, and Antibody Drug Conjugates (ADCs), monoclonal antibodies and bioconjugates, and adds new capabilities including mRNA.
• The new site focuses on minimizing environmental impact and uses advanced highly integrated and automated manufacturing systems.

Partner Examples

• Partner-backed pilots with organizations such as NVIDIA, AWS, robotics vendors, and camera and drone vendors.