Principal Laboratory Automation Scientist

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Job Function: R&D Product Development Job Sub Function: R&D Electrical/Mechatronic Engineering Job Category:
Scientific/Technology All Job Posting Locations:
Spring House, Pennsylvania, United States of America

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We are searching for the best talent for a Principal Laboratory Automation Scientist to be located in Spring House, PA.

The Principal Laboratory Automation Scientist sets the scientific automation architecture for integrated discovery platforms. The role defines how scientific requests, sample states, consumables, automated methods, data provenance, UAT, metadata, exception logic, and user adoption should work across interconnected workcells. The Principal Scientist will serve as a domain expert for advanced laboratory automation, multi-workcell orchestration, scientific workflow transformation, data provenance, metadata strategy, and AI-ready laboratory execution.

They will define the scientific workflow architecture that enables autonomous discovery platforms to generate trustworthy, reproducible, model-usable data. This role is paired with the Principal Automation and Robotics Engineer. The scientist owns scientific and data architecture for automated workflows; the engineer owns robotics, controls, safety, physical automation, serviceability, and machine architecture. Together they provide the core architectural partnership for self-driving laboratories.

Defining cross-workcell laboratory automation architecture spanning scientific requests, instruments, workcells, sample stores, consumable stores, scheduling layers, data systems, user interfaces, and model feedback loops. Creating standards for labware, sample identity, reagent identity, consumable identity, state models, method versioning, interface control, recovery logic, run context, data provenance, and operational handover. Establishing design patterns for request intake, automated worklist generation, queue management, workflow routing, workcell handoffs, exception handling, and data feedback into scientific decision making.

Defining acceptance criteria for nominal workflows, edge cases, failure modes, recovery scenarios, operator interventions, data completeness, and lights-out readiness from a scientific workflow perspective.

Defining scientific and technical requirements for integrated automation platforms supporting biologics discovery, small molecule discovery, protein engineering, screening,…