Physical AI Engineer - Lilly Medicine Foundry

Lilly • Indianapolis, IN

22h • $118,500 - $211,200

About The Position

The Physical AI Engineer will deploy advanced AI models into robotic, computer vision, and sensor-based systems at Lilly Medicine Foundry to improve safety, speed, and resilience. The role builds and integrates edge-to-cloud architecture connecting cameras, sensors, autonomous guided vehicles (AGVs), collaborative robots, drones, and quadrupeds to the Foundry data platform, manufacturing execution systems, and closed-loop quality systems. Simulation and digital twins will be used to validate systems before deployment in GMP areas. Initial pilots will run in existing spaces and labs before moving to the Foundry site.

The engineer will collaborate with AI researchers, hardware, OT/IT and automation engineers, Quality and HSE teams, safety experts, and technology and integration partners. The work includes developing model deployment and lifecycle practices, integrating systems with manufacturing and quality processes, validating performance and safety, and supporting production deployment. Solutions must be safe, secure, compliant, adaptable, and scalable in dynamic manufacturing environments.

Requirements

• Bachelor’s degree in Robotics, Computer Science, Electrical Engineering, Mechatronics, or a related technical field.
• At least 5 years of experience in robotics, automation, or AI-driven systems deployment in a manufacturing or industrial setting.
• Strong programming skills in Python, C++, or ROS (Robot Operating System).
• Strong analytical and problem-solving skills, including the ability to troubleshoot and optimize hardware and software components of robotic systems.
• Familiarity with industry safety standards and risk mitigation in robotics and automation, including standards such as ISO 10218, ISO 13482, ISO 3691-4, ISO 13849, and EN 61508, and OT cybersecurity practices such as IEC 62443.
• Excellent communication and teamwork skills, a customer-centric approach to technical challenges, and ability to collaborate with external partners and bridge IT, OT, Quality, HSE, and facilities teams.
• Solid knowledge of Computer System Validation and GxP requirements, including GAMP 5, 21 CFR Part 11, EU Annex 11, and validation of AI-enabled systems.
• Ability to analyze, anticipate, and resolve complex technical, operational, or business issues; prioritize and escalate issues effectively.
• Demonstrated learning agility and curiosity.

Nice To Haves

• Experience deploying computer vision or other AI models at the edge, including accelerated inference and video analytics pipelines, with models trained in the cloud; regulated or GMP manufacturing experience is especially relevant.
• Experience with safety verification methods and risk analysis for autonomous or semi-autonomous systems, including AGVs and mobile robots.
• Knowledge of AI frameworks such as TensorFlow, PyTorch, and OpenCV, and model optimization and serving tools such as ONNX, TensorRT, and Triton.
• Knowledge of industrial communication protocols such as OPC-UA, MQTT, Modbus, and Ethernet/IP.
• Experience with industrial control systems and safety controllers.
• Experience with edge AI and GPU platforms such as NVIDIA Jetson and DeepStream, and edge-to-cloud architectures for vision and sensor data.
• Experience with cloud services for IoT, data, and machine learning, such as AWS IoT Greengrass, SageMaker, S3, and GPU compute; familiarity with enterprise data platforms such as Databricks and Microsoft Fabric.
• Familiarity with OT/IT data integration and historian platforms such as Ignition, Aveva PI, and HighByte, and manufacturing execution systems.
• Familiarity with simulation and digital twin tools such as NVIDIA Omniverse, Isaac Sim, OpenUSD, and CAD, and synthetic data generation.
• Familiarity with generative AI, LLMs, and agent frameworks, including MCP, applied to operations, quality, or safety.
• Experience in pharmaceutical or biologics manufacturing operations, such as changeover, cleaning verification, or API processes, and integrating with quality and validation systems.
• Familiarity with data governance and AI risk-management practices for image, video, and sensor data.

Responsibilities

• Deploy and integrate machine learning, computer vision, generative AI, agent, and reinforcement learning models into robotic and sensing systems, including cameras, AGVs, co-bots, drones, and quadruped robots.
• Develop AI-enabled use cases including vision-based changeover, cleaning and dryness verification; spill, lockout/tagout, and intrusion detection; predictive maintenance; and site and lab environment monitoring.
• Design, build, and operate the edge-to-cloud reference architecture, including on-site GPU edge inference, cloud model training, OT/IT data integration, Foundry data platform ingestion, orchestration, and an ontology across the system layers.
• Evaluate build, buy, and hybrid options for edge hardware and edge-to-cloud software.
• Optimize AI algorithms and models for real-time performance on edge hardware, minimizing latency and ensuring robust behavior under varying conditions.
• Manage the model lifecycle, including training, versioning, performance monitoring, drift detection, and retraining.
• Build and maintain simulation-ready asset libraries from CAD data and the equipment registry, and use simulation platforms and digital twins to generate synthetic training data and test robot, co-bot, AGV behavior, and safety rules before deployment.
• Support twin-based use cases such as lockout/tagout and SOP guidance, equipment wayfinding, virtual tours, XR training, smart P&IDs, and maintenance.
• Integrate vision and sensor outputs with manufacturing execution and quality systems so detections enter an exception queue with an audit trail of Quality judgment on each AI flag.
• Expose equipment and twin data to AI agents and generate incident summaries for HSE and Quality review.
• Develop and implement verification methods, testing protocols, and performance benchmarks for AI-driven robotics and vision systems, including simulation-based validation and validation aligned with GxP and Computer System Validation requirements.
• Work with safety experts to establish and verify safety mitigations, safety rules for AGVs and co-bots, and integration with safety controllers; support HSE use cases such as lockout/tagout, near-miss analysis, and early incident detection.
• Ensure solutions pass cybersecurity architecture review, follow enterprise data governance and classification requirements for image, video, and sensor data, and complete AI intake and risk assessment before deployment.
• Oversee deployment of robotics and vision systems into production, move pilots from existing spaces and labs to the Foundry site as dependencies are met, and provide troubleshooting, technical support, and optimization.
• Collaborate with technology and integration partners, evaluate partner services for production readiness near GMP areas, bridge Digital, Robotics, Facilities, and Technology teams, and contribute to pilot selection and sequencing.
• Maintain documentation of deployment processes, safety protocols, validation and testing results, and AI integration methods; report system performance and improvements to stakeholders.

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, including 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, including an employee assistance program, fitness benefits, and employee clubs and activities.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior