This role pays less than
85%
of similar roles. Most pay
$162,000–$254,750
— the shaded band above.
At the midpoint, this role pays about
$142k
versus about
$208k
for comparable roles.
Based on 239 similar postings.
Employer
About Johnson & Johnson
Johnson & Johnson is a multinational corporation operating in three main segments: consumer health products, pharmaceuticals, and medical devices, known for brands like Tylenol, Band-Aid, and Janssen. Industry: Pharmaceuticals & Medical Devices
Johnson & Johnson currently has
46 open roles
on FindRole.
Listed pay typically runs
$117,000–$201,250
across 42 roles with salary data.
As a Senior Agentic Workflow Engineer in the Biologics Discovery team, you will build and maintain agentic workflows to automate secondary data processing and orchestrate analyses within the design-make-test-learn cycle. You will develop real-time pipelines connecting automation software, data stores, models, and compute environments while integrating experimental outputs into downstream decision-making processes. The role requires expertise in agentic AI, LLM-based agents, retrieval-augmented approaches like RAG or GraphRAG, and scientific workflow orchestration. You will collaborate with cross-functional teams to ensure data is semantically consistent and AI-ready. Key technical requirements include experience with MLOps, production-grade monitoring, and handling heterogeneous scientific data. This role addresses the challenge of automating biologics data flow to reduce manual intervention and improve reproducibility in a large pharmaceutical setting where physical and scientific AI intersect to drive discovery.
Build and maintain agentic workflows to automate secondary analysis for biologics discovery assays.
Develop real-time data pipelines connecting automation software, data stores, models, and compute environments.
Implement resilient APIs, observability, versioning, and workflow orchestration in partnership with IT teams.
Integrate enterprise Generative AI platforms and LLM-based agents into existing scientific workflows.
Ensure all automated data is high-quality, traceable, and semantically consistent for downstream use.
Collaborate with scientists and engineers to create closed-loop feedback systems within the design-make-test-learn cycle.
Identify opportunities to apply agentic AI to reduce manual intervention and improve experimental reproducibility.
What we're looking for
Master's or Ph.D. in Computer Science, Engineering, or a related computational field is required (or a Bachelor's with substantial relevant experience).
At least 2 years of experience building scientific workflow orchestration or automation software is required.
Experience in agentic workflows and applying LLM-based agents to scientific or laboratory workflows is required.
Hands-on experience designing real-time data pipelines and integrating complex, heterogeneous scientific data and instrument outputs is required.
Experience working with enterprise GenAI platforms and retrieval-augmented approaches like RAG or GraphRAG is required.
Strong communication skills and the ability to operate in a matrixed organization are required.
Preferred experience includes drug discovery domains such as biologics, high-throughput experimentation, or imaging.
Preferred exposure to lab automation, robotic systems, MLOps/DevOps, and FAIR data standards.
AI
Machine Learning
Data Science
Laboratory Automation
Digital Twin
Advanced Analytics
Cloud Computing
Process Modeling
Predictive Analytics
Advanced Process Control
Data Engineering
Data Governance
High-Throughput Experimentation
Knowledge Management
Digital Transformation
Remote (Foster City, CA)8 days ago
$169,320–$219,120
Python
PyTorch
JAX
Machine Learning
Deep Learning
Generative Modeling
Protein Language Models
Foundation Models
Geometric Neural Networks
AlphaFold
Active Learning
Multimodal Learning
Representation Learning
Data Integration
Design-Build-Test-Learn