Senior Machine Learning Engineer, Biologics Discovery

We need to output HTML fragment with sections: headings

for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We need to extract content from posting. We must not repeat job title as heading. No Job ID etc. Remove boilerplate: EEO, legal, application instructions, etc. We need to preserve all substantive info. Let's parse posting: - Intro about J&J (mission) – maybe part of "About the role"? Could be considered "About the role" or "About the company". The instruction says use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have "About the role" heading with description of role. - "About The Opportunity" section: description of Senior ML Engineer role, building and operates integration, deployment, lifecycle management, governance capabilities. Also location info. - "Why This Role Matters": description. - "Position Summary": description. - "Why This Role Is Unique": description. - "Key Responsibilities": bullet list. - "Reliability, Observability & Scale": bullet list. - "Partnership & Standards": bullet list. - "Ideal Candidate Profile": description. - "Qualifications": Required and Preferred. - "Required Skills Preferred Skills:" list of skills. - Pay: "The anticipated base pay range for this position is $109,000 to $174,800." Also mention bonus etc. That's pay info. - Benefits: list of benefits: medical, dental, vision, life insurance, short- and long-term disability, business accident insurance, group legal insurance; consolidated retirement plan (pension) and savings plan (401(k)); time off benefits: vacation up to 120 hours, sick time up to 40 hours, holiday pay up to 13 days, personal/family time up to 40 hours. - Schedule: not mentioned explicitly; maybe not include. - Location info: location list; could be part of "About the role" or separate "Location". But we only have allowed headings; location could be part of About the role or maybe we can include a "Location" section? Not in list; but we could include under About the role. We must not create sections with filler like "not specified". So only include sections where we have content. Thus sections: About the role, Responsibilities, Requirements (maybe combine Qualifications?), Qualifications, Skills, Benefits, Pay. We need to decide mapping: - About the role: include mission, opportunity description, why role matters, position summary, why unique, ideal candidate profile? Might be too much but okay. But we also have separate Qualifications and Skills sections. We could structure:

About the role

... description of role, mission, location, etc.

Responsibilities

• ... combine all responsibility bullet points from Key Responsibilities, Reliability, Observability & Scale, Partnership & Standards.

Qualifications

Required: ... list. Preferred: ... list.

Skills

• list of required/preferred skills from "Required Skills Preferred Skills:"

Benefits

• list benefits

Pay

Base pay range $109,000 to $174,800, plus annual performance bonus eligibility, etc.

We should not include schedule. We must not duplicate content. Ensure we preserve all substantive info. Let's extract details. About the role content: From posting: - "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." That's company overview. - "Job Function R&D Product Development Job Sub Function R&D Machine Learning Job Category Scientific/Technology All Job Posting Locations: Beerse, Antwerp, Belgium, Madrid, Spain, Raritan, New Jersey, United States of America, Spring House, Pennsylvania, United States of America, Titusville, New Jersey, United States of America" But we should not repeat job title; location can be included. - "About The Opportunity Johnson & Johnson Innovative Medicine is seeking a Senior ML Engineer for our Biologics Discovery Data Science team. This role builds and operates the integration, deployment, lifecycle management, and governance capabilities that enable machine learning (ML) models and AI solutions developed by partner organizations to run reliably in Biologics Discovery environments. You are the senior team member who closes the gap between model-ready data in our data warehouse and models that serve discovery scientists. This position will be based at one of our office locations in either Spring House, PA (strongly preferred), Titusville, NJ, or Raritan, NJ, USA; Beerse, Belgium, or Madrid, Spain. (No remote option.) Please note that this role is available across multiple countries and may be posted under different requisition numbers to comply with local requirements. While you are welcome to apply to any or all of the postings, we recommend focusing on the specific country(s) that align with your preferred location(s): USA - Requisition Number: R-099962 Spain - Requisition Number: R-100646 Belgium - Requisition Number: R-100647" We should not include requisition numbers (metadata). So drop those lines. - "Why This Role Matters The future of AI-native discovery depends on high-quality AI and data solutions that connect scientific data, machine learning models, and agentic workflows. This role will shape how enterprise AI and MLOps capabilities are adapted, integrated, and operationalized for Biologics Discovery, enabling AI solutions to scale from prototypes into trusted capabilities that accelerate scientific learning and therapeutic discovery." - "Position Summary In this role, you will enable AI/ML solutions to move reliably from development into production within Biologics Discovery. Working closely with data scientists, AI/ML scientists, discovery scientists, and partner organizations at J&J, you will own the deployment, lifecycle management, access, monitoring, and governance of ML, generative AI, and agentic solutions for discovery workflows. The role does not own core model development or the underlying enterprise platforms. Instead, it ensures that models and AI capabilities developed by partner teams are operationalized reliably for scientific use. You will bring expertise in modern AI/ML operational practices, including reproducibility, CI/CD, observability, governance, automation, and scalable compute, helping adapt enterprise capabilities for discovery-specific use cases. Your work will enable reliable, production-grade AI workflows and accelerate the adoption of ML and agentic systems in biologics discovery." - "Why This Role Is Unique This is a rare opportunity to play a key role in enabling AI-native Biologics Discovery. You will help operationalize and scale AI/ML capabilities that transform model-ready data and promising models into reliable, production-grade solutions that accelerate scientific discovery." - "Ideal Candidate Profile The ideal candidate is a pragmatic AI/ML practitioner who combines operational expertise with a passion for reliability, reproducibility, and automation. They thrive at bridging scientific needs and technical capabilities, enabling AI/ML solutions to move efficiently from experimentation to trusted scientific impact." We can combine these into About the role paragraph. Now Responsibilities: we have three subsections: Key Responsibilities, Reliability, Observability & Scale, Partnership & Standards. Each contains bullet-like sentences. Let's extract each bullet: Key Responsibilities: - "AI/ML Operations & Lifecycle Management Build and operate scalable pipelines and interfaces that deliver model-ready data to ML, generative AI, and agentic workflows." - "Enable closed-loop scientific learning by ensuring newly generated scientific data can be captured, governed, and made available to downstream modeling, evaluation, and agentic workflows." - "Establish reliable operational capabilities for model deployment, serving, monitoring, access management, and lifecycle management across development and production environments." - "Implement model, data, and workflow versioning, with reproducible releases, rollback capabilities, and traceability across the AI/ML lifecycle." Reliability, Observability & Scale: - "Establish monitoring, observability, alerting, and performance management practices for ML workflows, deployed models, and AI services." - "Develop and maintain automated workflows supporting testing, release management, environment management, and operational excellence across AI/ML solutions." - "Enable AI capabilities to scale with growing scientific data volumes, computational demands, and increasingly autonomous discovery workflows." - "Monitor model and system behavior in production, including data quality, model performance, drift, latency, reliability, and resource utilization." Partnership & Standards: - "Partner with data scientists, technology teams, and domain experts to establish reliable integration patterns between scientific data products and AI/ML workflows." - "Enable ML scientists and AI agents with reproducible training, fine-tuning, evaluation, experimentation, and deployment capabilities." - "Establish reusable patterns, best practices, and standards that accelerate the transition from experimentation to production deployment." - "Contribute to security, access control, AI governance, documentation, and cost management practices across AI/ML solutions." We'll put each as

. Qualifications: Required: - "Degree in Computer Science, Engineering, Data Science, Machine Learning, or a related computational field." - "4+ years of experience operationalizing and scaling AI/ML solutions in production environments, including ML, generative AI, or agentic workflows." - "Strong proficiency in Python, with experience developing AI/ML workflows for model training, fine-tuning, evaluation, deployment, and serving." - "Experience with cloud infrastructure and modern data platforms used to support AI/ML workloads." - "Expertise with model registries, experiment tracking, and ML lifecycle management tools (e.g., MLflow, Weights & Biases)." - "Experience implementing production AI/ML practices, including model versioning, deployment automation, CI/CD, automated testing, observability, monitoring, containers, orchestration technologies, and scalable compute environments." - "Strong software development and automation practices, with the ability to partner effectively with data scientists, AI/ML practitioners, technology teams, and domain experts." Preferred: - "Experience in pharmaceutical, biotechnology, or life sciences sectors." - "Exposure to real-time/near-real-time pipelines and instrument data integration." - "Experience working with FAIR data principles, metadata management, data lineage, provenance, and AI-ready data practices." We'll present as

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