below marketmost similar roles pay hereabove market
This role pays less than
68%
of similar roles. Most pay
$211,724–$285,383
— the blue band above.
At the midpoint, this role pays about
$223k
versus about
$249k
for comparable roles.
Based on 240 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
$113,000–$201,250
across 38 roles with salary data.
The Director, Computer-Directed Drug Design and AI-Native Molecular Design joins the In Silico Drug Discovery team to lead a portfolio-embedded team advancing AI-native scientific discovery for small-molecule therapeutic programs. This leader co-owns molecular design strategy, translating target product profiles into integrated computational and experimental plans. Key responsibilities include orchestrating design–make–test–learn cycles, evaluating AI-generated proposals, and scaling repeatable learning systems to improve compound quality across various modalities like degraders, peptides, and molecular glues. The role requires deep expertise in computational chemistry, cheminformatics, predictive modeling, and protein structure prediction. Candidates must demonstrate proficiency in multiparameter optimization and medicinal chemistry judgment. This scientific leadership position focuses on integrating computational models with experimental data to drive discovery decisions and improve portfolio impact within the innovative medicine space.
Co-own program strategy, molecular design priorities, and critical discovery decisions with medicinal chemistry and biology leaders.
Translate target product profiles and biological hypotheses into integrated computational and experimental strategies.
Lead a portfolio-embedded team of computational molecular architects to ensure predictive models guide compound decisions.
Orchestrate repeatable design–make–test–learn cycles by defining scientific questions, decision criteria, and feedback loops.
Critically assess AI-generated proposals for chemical tractability, data quality, reproducibility, and responsible-use controls.
Integrate the CDDD team with In Silico Discovery partners to define use cases and prioritize scalable platforms.
Drive adoption of validated AI-native capabilities through practical deployment, training, and transparent metrics.
Lead, coach, and develop a high-performing team to strengthen molecular architect judgment and AI-native scientific capability.
What we're looking for
PhD in computational chemistry, theoretical chemistry, chemical physics, biophysics, cheminformatics, medicinal chemistry, or a related scientific discipline.
Minimum of 8 years of relevant small-molecule drug-discovery experience, including progressive scientific and people leadership.
Demonstrated impact across multiple discovery programs from hit identification through lead optimization, including contribution to a major progression milestone or candidate nomination.
Deep applied expertise in computational chemistry, cheminformatics, molecular design, and multiparameter optimization.
Strong medicinal chemistry and molecular architecture judgment to balance potency, selectivity, pharmacokinetics, safety, and synthesis feasibility.
Proven ability to co-lead in a matrix, influence governance, and build effective partnerships across portfolio-embedded scientific teams.
Proven people leader with experience building, coaching, and scaling an application-focused computational drug-discovery team.
Experience across multiple small-molecule modalities, such as traditional Rule-of-Five and beyond-Rule-of-Five molecules, degraders, RIPTACs, molecular glues, and peptides (preferred).
Python
Cheminformatics
Computational Chemistry
Medicinal Chemistry
Large Language Models
AI
Workflow Orchestration
Version Control
Data Science
Predictive Models
Chemogenomics
CADD
Scientific Workflows
Applied AI
LLMs
RAG
MLOps
LLMOps
Agentic Systems
CI/CD
Infrastructure as Code
Knowledge Graphs
Semantic Search
Multimodal AI
Distributed Systems
APIs
Cloud Services
Observability
Data Contracts
MCP
A2A
Causal Inference
Machine Learning
RNA Therapeutics
BioNeMo
Data Curation
Platform Engineering
Automation
Computational Biology
Active Learning
Foundation Models
Data Lineage
Feature Stores
Simulation
Data Analysis