Senior Machine Learning Engineer, Biologics Discovery

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 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 Job Description: 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 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; Beers, 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 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. 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. 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. 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 prin