Engineer, Digital and Data Science – Robotics and Automation We are seeking an engineer to execute software, data, systems-integration, and digital-connectivity work required to develop and operate advanced robotic and automation platforms supporting AZD0120 and future cell and gene therapy programs. This engineer will build and support reliable interfaces, reusable software components, data pipelines, workflow logic, monitoring tools, and technical documentation that connect robotics with laboratory, manufacturing, and enterprise systems. The role will support platform scaling and the transition of robotic and digitally enabled workflows from laboratory development through manufacturing implementation and deployment for future commercial production. The successful candidate will work hands-on in laboratory and engineering environments with automation engineers, scientists, Manufacturing Science and Technology, QC, Manufacturing Operations, Quality, Digital, IT, Informatics, and external technology partners. The engineer will directly support laboratory testing, engineering runs, workflow execution, equipment operation, troubleshooting, and data review in addition to software and data responsibilities. A central expectation is disciplined collaboration with Digital and IT so that locally useful solutions are also secure, supportable, governed, interoperable, and aligned with enterprise architecture and data standards. This position reports to the Executive Director, Automation, Robotics & Technical Writing and is based in Tarzana, California.
Accountabilities:
Software Engineering for Robotics and Automation Develop, configure, test, deploy, and maintain software components, scripts, utilities, services, and interfaces supporting robotic and modular automation workflows. Implement equipment and instrument connectivity using approved APIs, software development kits, middleware, message-based integration, database interfaces, equipment drivers, serial communication, or industrial protocols, as applicable. Contribute to workflow orchestration, scheduling logic, device-state management, handshakes, alarms, exception handling, pause-and-resume behavior, and recovery pathways. Apply maintainable software-engineering practices using Digital- and IT-approved technology stacks, development environments, repositories, source-control and peer-review practices, automated testing, CI/CD approaches, release documentation, configuration management, and controlled deployment. Develop within approved enterprise technology stacks and integration patterns, which may include Git-based repositories, Azure DevOps or comparable lifecycle tooling, APIs, containerized applications, cloud services, automated test frameworks, observability tooling, and software release-management capabilities, as appropriate to the intended use and support model. Troubleshoot software defects, interface failures, timing issues, data mismatches, device-state conflicts, and workflow interruptions using logs, diagnostics, test evidence, and structured root-cause analysis. Create reusable integration patterns and libraries that reduce one-off customization and improve scalability across automation platforms.
Data Engineering, Contextualization and Traceability:
Design and maintain data pipelines that capture, validate, transform, contextualize, store, and expose robotics- and equipment-generated data for authorized scientific, engineering, and operational use. Define and implement data structures for process parameters, equipment states, alarms, workflow events, sample and material identity, metadata, user actions, timestamps, and execution history. Support end-to-end sample and material lineage, data provenance, auditability, and traceability across connected workflows and systems. Build dashboards, automated reports, alerts, and engineering views that provide visibility into workflow status, failures, cycle time, utilization, interventions, and system performance without replacing accountable business or quality records. Perform data-quality checks and support reconciliation of missing, duplicated, delayed, incorrectly mapped, or out-of-range data. Partner with data platform owners to align interfaces, metadata, retention, access, and data-product practices with enterprise standards.
Digital and IT Partnership, Architecture and Cybersecurity:
Work with Digital, IT, Informatics, system owners, and enterprise architects to translate scientific and automation needs into secure, supportable software and data solutions. Bridge Operational Technology (OT), Laboratory Technology (LT), and Information Technology (IT) environments to enable secure, interoperable, and scalable automation ecosystems spanning laboratory, manufacturing, and enterprise systems. Contribute to interface contracts, data-flow diagrams, network and hosting requirements, identity and access models, environment strategies, backup and recovery expectations, monitoring, and support handoffs. Collaborate with Manufacturing, Quality, Digital, and IT to integrate robotics and automation platforms with manufacturing execution systems (MES), electronic batch records, LIMS, process historians, manufacturing analytics platforms, and related systems supporting cGMP operations. Use approved development environments, repositories, cloud services, integration patterns, and deployment processes; avoid unsupported shadow systems and unmanaged production dependencies. Support cybersecurity reviews, threat and vulnerability remediation, least-privilege access, secrets management, patching coordination, logging, incident support, and lifecycle planning for connected automation assets. Clearly define ownership boundaries among PSAT, vendors, Digital, IT, Quality, and business system owners, including escalation pathways and operational support responsibilities.
AI-Enabled Robotics and Advanced Analytics:
Collaborate with Digital, IT, Data Science, Quality, and scientific SMEs to evaluate AI/ML use cases for robotics monitoring, anomaly detection, predictive maintenance, computer vision, scheduling, workflow optimization, and operator decision support. Support development and application of digital twins, simulation environments, virtual commissioning models, workflow emulation, and robotics or process models that improve design evaluation, capacity planning, troubleshooting, optimization, deployment readiness, and lifecycle improvement. Evaluate and support computer-vision capabilities for robotic guidance, barcode and label verification, object and container identification, material tracking, automated inspection, anomaly detection, and workflow monitoring, subject to defined intended use, testing, and human oversight. Prepare trusted, contextualized, and appropriately governed data suitable for model development, evaluation, and monitoring. Prototype AI-enabled capabilities only within approved environments and with defined intended use, acceptance criteria, human oversight, source traceability, access controls, and lifecycle ownership. Evaluate model and workflow performance using documented test data; identify limitations, false positives, drift, unsupported outputs, and operational risks before broader deployment. Ensure AI outputs remain advisory unless specifically approved for another intended use, and ensure qualified personnel retain accountability for scientific, operational, quality, and regulatory decisions. Contribute to reusable AI integration patterns, model interfaces, monitoring approaches, and documentation that enable responsible scale-up across robotics platforms.
Robotics Platform and Process-Workflow Enablement:
Configure and support software and data integration for platforms such as Multiply Labs integrated robotic manufacturing systems, HighRes Biosolutions, Hamilton, and comparable robotic or modular automation systems. Support development and scaling of advanced robotic cell therapy manufacturing architectures, including modular automation, integrated robotic process cells, orchestration across unit operations, and future-state robotic cell therapy factory concepts using platforms such as Multiply Labs and comparable technologies. Partner with scientists and automation engineers to map current and future workflows and convert process requirements into software behavior, interface requirements, data requirements, and testable acceptance criteria. Support connected workflows involving cell processing, liquid handling, incubation, centrifugation, sampling, analytical testing, barcode identification, imaging, material movement, and related operations. Participate in prototyping, workflow simulations, engineering runs, feasibility studies, equipment testing, commissioning, and operational-readiness activities. Support the transition from prototype and laboratory automation to manufacturing-ready solutions by strengthening robustness, repeatability, reliability, maintainability, cybersecurity, data integrity, supportability, capacity, and operational readiness. Develop practical tools and user interfaces that improve usability while preserving approved process intent, data integrity, and appropriate operator controls. Contribute to deployment planning, technology transfer, site implementation, commissioning, qualification, support-model definition, and lifecycle management for automation intended to support future clinical and commercial production.
Hands-On Laboratory Testing and Workflow Execution:
Perform hands-on work in laboratory and engineering environments to support development, integration, and testing of robotic and automation workflows. Execute laboratory studies and engineering runs using automated and manual methods to assess workflow feasibility, software behavior, equipment integration, sample handling, data capture, and operational usability. Operate and support laboratory and automation equipment used for cell processing, liquid handling, centrifugation, incubation, sampling, analytical testing, barcode identification, imaging, and material movement, following applicable procedures and training requirements. Prepare equipment, instruments, consumables, samples, reagents, and test materials for automation development, integration testing, FAT/SAT support, commissioning, qualification, and workflow demonstrations. Collect and review experimental, equipment, software, and workflow data; compare observed results with defined requirements and acceptance criteria; and document anomalies, limitations, and recommended improvements. Partner with scientists and automation engineers to evaluate manual-to-automated workflow translation, including sequence
