Senior Machine Learning Engineer, Biologics Discovery

Johnson & Johnson

. Build and operate scalable pipelines and interfaces that deliver model-ready data to ML, generative AI, and agentic workflows .

Posted 9/18/2026full-timeSpring House • New Jersey • United StatesSenior💰 $109,000 - $174,800 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in operationalizing and scaling AI/ML solutions, with a strong focus on model deployment, monitoring, and lifecycle management. Proficient in developing automated workflows and ensuring data quality and performance in production environments.

Highest-signal resume keywords

Python ProficiencyAI/ML Workflow DevelopmentModel Versioning and Deployment AutomationCloud Infrastructure ExperienceML Lifecycle Management Tools Expertise

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Model DeploymentData Quality MonitoringAutomated TestingCI/CD PracticesObservability and MonitoringScalable Compute EnvironmentsExperiment TrackingData LineageReproducible TrainingFine-Tuning and Evaluation

Soft Skills

Collaboration with Data ScientistsPartnership with Technology TeamsCommunication with Domain Experts

Tools & Technologies

MLflowWeights & BiasesContainersOrchestration TechnologiesModern Data Platforms

Industry Keywords

Pharmaceutical SectorBiotechnology SectorLife Sciences SectorFAIR Data PrinciplesAgentic Workflows

Tech Stack

Tools & technologies

CloudPython

About the role

Key responsibilities & impact

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 capturing, governing, and making newly generated scientific data available to downstream modeling, evaluation, and agentic workflows
Establish 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 lifecycle traceability
Establish monitoring, observability, alerting, and performance management practices for ML workflows, deployed models, and AI services
Develop and maintain automated workflows for testing, release management, environment management, and operational excellence
Enable AI capabilities to scale with scientific data volumes, computational demands, and autonomous discovery workflows
Monitor production model and system behavior, including data quality, model performance, drift, latency, reliability, and resource utilization
Partner with data scientists, technology teams, and domain experts to establish integration patterns between scientific data products and AI/ML workflows
Enable reproducible training, fine-tuning, evaluation, experimentation, and deployment capabilities
Establish reusable patterns, best practices, and standards for transitioning experimentation to production
Contribute to security, access control, AI governance, documentation, and cost management practices

Requirements

What you’ll need

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
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 such as MLflow and 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
Experience partnering with data scientists, AI/ML practitioners, technology teams, and domain experts
Experience in pharmaceutical, biotechnology, or life sciences sectors (preferred)
Exposure to real-time/near-real-time pipelines and instrument data integration (preferred)
Experience with FAIR data principles, metadata management, data lineage, provenance, and AI-ready data practices (preferred)

Benefits

Comp & perks

Annual performance bonus eligibility
Medical insurance
Dental insurance
Vision insurance
Life insurance
Short- and long-term disability insurance
Business accident insurance
Group legal insurance
Pension plan
401(k) savings plan
Vacation – up to 120 hours per calendar year
Sick time – up to 40 hours per calendar year
Holiday pay, including Floating Holidays – up to 13 days per calendar year
Work, Personal and Family Time – up to 40 hours per calendar year
Inclusive interview accommodations for applicants with disabilities