Principal Lab Automation Scientist — Autonomous Discovery

Johnson & Johnson; Spring House (PA); On-site; USD 180,000 - 240,000; Full time

Job summary

Johnson & Johnson in Spring House, PA, seeks a Principal Laboratory Automation Scientist to define the automation architecture for integrated discovery platforms. The role focuses on cross-workcell orchestration and scalable data-driven workflows.

You will lead scientific workflow transformation, data provenance strategies, and AI-ready implementation, collaborating across engineering, biology, and analytics teams to enable trustworthy, reproducible experiments.

Responsibilities

• Define cross-workcell laboratory automation architecture spanning scientific requests, instruments, workcells, sample stores, consumable stores, scheduling layers, data systems, user interfaces, and model feedback loops.
• Establish design patterns for request intake, automated worklist generation, queue management, workflow routing, workcell handoffs, exception handling, and data feedback into scientific decision making.
• Define acceptance criteria for nominal workflows, edge cases, failure modes, recovery scenarios, operator interventions, data completeness, and lights-out readiness from a scientific workflow perspective.
• Define scientific and technical requirements for integrated automation platforms supporting biologics discovery, small molecule discovery, protein engineering, screening, analytical sciences, NGS, cell-based assays, biophysics, protein characterization, and translational workflows.
• Lead throughput modeling, queue modeling, workflow simulation, digital twin strategy, AI-ready metadata, experimental provenance, sample lineage, failure annotations, and data contracts.

Job description

Johnson & Johnson in Spring House, PA, seeks a Principal Laboratory Automation Scientist to define the automation architecture for integrated discovery platforms. The role focuses on cross-workcell orchestration and scalable data-driven workflows.

You will lead scientific workflow transformation, data provenance strategies, and AI-ready implementation, collaborating across engineering, biology, and analytics teams to enable trustworthy, reproducible experiments.