Principal Laboratory Automation Scientist

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

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at jnj.com .

As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

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

Job Description:

About Innovative Medicine

Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.

Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.

Learn more at https://www.jnj.com/innovative-medicine

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

Purpose:

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.

You will be responsible for:

Reference Architecture and Standards

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.

Scientific Leadership and Workflow Transformation

Defining 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.

Resolving complex issues spanning scientific method performance, liquid handling, assay suitability, automation constraints, orchestration, data flow, sample logistics, software behavior, facilities constraints, biosafety, and vendors.

Leading cross-functional investigations and improvement programs that increase reliability, scalability, data quality, throughput, scientific adoption, and model feedback utility.

Advising senior partners on workflow maturity, automation readiness, manual versus automated boundaries, standardization opportunities, and scientific risks before major investment decisions.

SDL Enablement and AI-Ready Data

Leading throughput modeling, queue modeling, workflow simulation, digital twin strategy, AI-ready metadata, experimental provenance, sample lineage, failure annotations, and data contracts.

Shaping roadmaps for autonomous execution, physical AI integration, closed-loop experimentation, human oversight, human intervention guardrails, and model feedback.

Defining data quality expectations that connect instrum