Principal Laboratory Automation Scientist
for section headings. Use - for any list of duties/requirements/benefits, and
for prose. Do NOT repeat job title as heading. Do NOT put Job ID etc. Remove boilerplate (EEO, etc). Include only sections with real specific content.
We need to preserve all substantive info. Let's extract content.
First paragraph about J&J mission etc - maybe part of "About the role"? Actually "About Innovative Medicine" is description of division. Could be "About the role". We'll include that as prose.
Then "We are searching for the best talent for a Principal Laboratory Automation Scientist to be located in Spring House, PA." That's location info; could be part of About the role.
Then "Purpose: ..." That's description of role purpose; could be part of About the role or Responsibilities.
Then "You Will Be Responsible For" with bullet-like paragraphs (each sentence). We'll turn into
- .
Then "Qualifications/Requirements" with education and experience. We'll split into Requirements (education) and Skills maybe.
Then "Other Travel may be required..." That's maybe a note under Responsibilities or separate? Could be part of Responsibilities.
Then "Why Join Us" paragraph about role supporting transition etc. That's maybe Benefits? Actually it's more about role impact; could be "About the role" or separate "Why Join Us" but not in allowed headings. Could be placed under "About the role" as prose.
We must not create Benefits, Pay, Schedule unless present. Not present.
We must not include EEO boilerplate etc. Remove that.
We must not include "Required Skills Preferred Skills:" at end (looks like incomplete). Remove.
Now craft HTML.
Structure:
About the role
... mission text ...
... location ...
Purpose: ...
But purpose could be its own paragraph.
Better: About the role includes mission, location, purpose.
Then Responsibilities
- Reference Architecture and Standards ...
etc.
We need to capture each bullet from "You Will Be Responsible For". The text includes several paragraphs each starting with a bold heading? Actually they are sentences with colon? Let's extract:
- 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 instrument outputs, sample context, run metadata, environmental context, and scientific interpretation.Mentoring laboratory automation scientists and influence communities of practice around method robustness, data provenance, automated workflow design, and adoption.
- Additional Responsibilities Support technology evaluations, proof-of-concept studies, workflow readiness assessments, user demonstrations, and vendor evaluations for future automation capabilities.Contribute to laboratory automation standards, workflow templates, data handoff expectations, training materials, and playbooks.Support audit, inspection, or quality documentation requests where automated scientific workflows intersect with regulated or quality driven environments. Participate in lessons learned and continuous improvement activities to improve future automation delivery and adoption.
We could split each bullet into separate - items.
Now Qualifications/Requirements:
Education: A minimum of a Bachelor's degree in Life Science, Analytical Science, Engineering, Computer Science, Laboratory Automation, or a related field is required. An advanced degree is strongly preferred.
Experience And Skills Required: Bachelor's degree with 10+ years, Master's degree with 8+ years, or PhD with 5+ years of relevant laboratory automation, scientific automation, analytical automation, high-throughput discovery, or workcell integration experience.Demonstrated understanding of laboratory workflows, sample handling, plate/tube formats, assay execution, consumable preparation, run documentation, and data capture.Hands-on or practical experience with automated liquid handlers or integrated laboratory automation equipment appropriate to the level of the role.Ability to translate scientific workflows into executable automated methods, worklists, run states, exception pathways, and user-facing procedures.Working knowledge of sample identity, labware identity, reagent identity, consumable state, barcode traceability, metadata capture, and downstream data requirements.Ability to collaborate with scientists, automation engineers, IT, data science, AI, EHS, facilities, vendors, and laboratory operations.Recognized expertise in laboratory automation, integrated workcells, assay automation, workcell orchestration, scientific workflows, and data flow.Ability to define architecture, standards, interface models, recovery logic, and data requirements across multidisciplinary teams. Preferred Experience with SDLs, digital twins, FAIR data, AI-ready metadata, SiLA 2, OPC UA, APIs, event-driven architectures, microservices, workflow engines, and Tier 1 integrator models.Experience with biologics discovery, small molecule discovery, protein engineering, high-throughput screening, analytical sciences, NGS, cell-based assays, and model feedback workflows.Record of scientific or technical innovation through platform deployments, invention disclosures, patents, publications, or external collaborations.
Other Travel may be required for vendor assessments, FAT/SAT activities, site visits, system builds, workshops, training, technology scouting, or cross-site deployment support.
We could treat Education as Requirements, Experience and Skills as Skills, and Travel as maybe Responsibilities? But travel is not a duty; maybe put under Responsibilities as additional note.
But we already have Responsibilities section; we could add a
- for travel.
Better: Keep Travel under Responsibilities as an additional bullet.
Now "Why Join Us" paragraph: This role supports the transition from isolated automation projects to integrated, reliable, AI-native, and ultimately self-driving discovery laboratory systems. The work helps connect scientific intent with robust physical execution, reproducible data, model feedback, and scalable operational capability.
We could put that under About the role as prose.
Now ensure we don't include boilerplate like EEO etc.
Now produce HTML.
We'll have:
About the role
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
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.
Then Responsibilities:
Responsibilities
- 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 instrument outputs, sample context, run metadata, environmental context, and scientific interpretation.Mentoring laboratory automation scientists and influence communities of practice
for prose. Do NOT repeat job title as heading. Do NOT put Job ID etc. Remove boilerplate (EEO, etc). Include only sections with real specific content. We need to preserve all substantive info. Let's extract content. First paragraph about J&J mission etc - maybe part of "About the role"? Actually "About Innovative Medicine" is description of division. Could be "About the role". We'll include that as prose. Then "We are searching for the best talent for a Principal Laboratory Automation Scientist to be located in Spring House, PA." That's location info; could be part of About the role. Then "Purpose: ..." That's description of role purpose; could be part of About the role or Responsibilities. Then "You Will Be Responsible For" with bullet-like paragraphs (each sentence). We'll turn into
- .
Then "Qualifications/Requirements" with education and experience. We'll split into Requirements (education) and Skills maybe.
Then "Other Travel may be required..." That's maybe a note under Responsibilities or separate? Could be part of Responsibilities.
Then "Why Join Us" paragraph about role supporting transition etc. That's maybe Benefits? Actually it's more about role impact; could be "About the role" or separate "Why Join Us" but not in allowed headings. Could be placed under "About the role" as prose.
We must not create Benefits, Pay, Schedule unless present. Not present.
We must not include EEO boilerplate etc. Remove that.
We must not include "Required Skills Preferred Skills:" at end (looks like incomplete). Remove.
Now craft HTML.
Structure:
About the role
... mission text ...
... location ...
Purpose: ...
But purpose could be its own paragraph. Better: About the role includes mission, location, purpose. ThenResponsibilities
- Reference Architecture and Standards ... etc. We need to capture each bullet from "You Will Be Responsible For". The text includes several paragraphs each starting with a bold heading? Actually they are sentences with colon? Let's extract: - 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 instrument outputs, sample context, run metadata, environmental context, and scientific interpretation.Mentoring laboratory automation scientists and influence communities of practice around method robustness, data provenance, automated workflow design, and adoption. - Additional Responsibilities Support technology evaluations, proof-of-concept studies, workflow readiness assessments, user demonstrations, and vendor evaluations for future automation capabilities.Contribute to laboratory automation standards, workflow templates, data handoff expectations, training materials, and playbooks.Support audit, inspection, or quality documentation requests where automated scientific workflows intersect with regulated or quality driven environments. Participate in lessons learned and continuous improvement activities to improve future automation delivery and adoption. We could split each bullet into separate
- items. Now Qualifications/Requirements: Education: A minimum of a Bachelor's degree in Life Science, Analytical Science, Engineering, Computer Science, Laboratory Automation, or a related field is required. An advanced degree is strongly preferred. Experience And Skills Required: Bachelor's degree with 10+ years, Master's degree with 8+ years, or PhD with 5+ years of relevant laboratory automation, scientific automation, analytical automation, high-throughput discovery, or workcell integration experience.Demonstrated understanding of laboratory workflows, sample handling, plate/tube formats, assay execution, consumable preparation, run documentation, and data capture.Hands-on or practical experience with automated liquid handlers or integrated laboratory automation equipment appropriate to the level of the role.Ability to translate scientific workflows into executable automated methods, worklists, run states, exception pathways, and user-facing procedures.Working knowledge of sample identity, labware identity, reagent identity, consumable state, barcode traceability, metadata capture, and downstream data requirements.Ability to collaborate with scientists, automation engineers, IT, data science, AI, EHS, facilities, vendors, and laboratory operations.Recognized expertise in laboratory automation, integrated workcells, assay automation, workcell orchestration, scientific workflows, and data flow.Ability to define architecture, standards, interface models, recovery logic, and data requirements across multidisciplinary teams. Preferred Experience with SDLs, digital twins, FAIR data, AI-ready metadata, SiLA 2, OPC UA, APIs, event-driven architectures, microservices, workflow engines, and Tier 1 integrator models.Experience with biologics discovery, small molecule discovery, protein engineering, high-throughput screening, analytical sciences, NGS, cell-based assays, and model feedback workflows.Record of scientific or technical innovation through platform deployments, invention disclosures, patents, publications, or external collaborations. Other Travel may be required for vendor assessments, FAT/SAT activities, site visits, system builds, workshops, training, technology scouting, or cross-site deployment support. We could treat Education as Requirements, Experience and Skills as Skills, and Travel as maybe Responsibilities? But travel is not a duty; maybe put under Responsibilities as additional note. But we already have Responsibilities section; we could add a
- for travel.
Better: Keep Travel under Responsibilities as an additional bullet.
Now "Why Join Us" paragraph: This role supports the transition from isolated automation projects to integrated, reliable, AI-native, and ultimately self-driving discovery laboratory systems. The work helps connect scientific intent with robust physical execution, reproducible data, model feedback, and scalable operational capability.
We could put that under About the role as prose.
Now ensure we don't include boilerplate like EEO etc.
Now produce HTML.
We'll have:
About the role
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
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.
Then Responsibilities:Responsibilities
- 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 instrument outputs, sample context, run metadata, environmental context, and scientific interpretation.Mentoring laboratory automation scientists and influence communities of practice