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# Salesforce Developer

- **Company:** CareerCircle
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $129,000.0 - $203,100.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Data Analysis, Automation of Tests, Microsoft Azure, Bioinformatics, Computational Biology, Information Engineering, Data Visualization, R (Programming Language), Monitoring of Systems, Python (Programming Language), Machine Learning, Language Modeling, Modular Design, Performance Tuning, Tensorflow, Software Engineering, SQL Databases, Systems Integration, Test Case, Reinforcement Learning, Data Processing, High Performance Computing, Pytorch, Large Language Models, Multi-Agent Systems, Prompt Engineering, Deep Learning, Information Technology, Machine Learning Operations, Virtual Agents, Api Design, Software Version Control, Recurrent Neural Networks, Programming Languages
- **Published:** August 8, 2026
- **Apply:** https://www.careercircle.com/jobs/all/all/usa/pa/west-point/7da19a21-3f6f-44f9-8c79-12864594aa90

## About the Role

* Ph.D. or M.S. in Computer Science, Computational Biology, Computational Chemistry, Bioinformatics, Statistics, or related field.
 * 0+ years post-PhD or 3+ years post-MS experience developing and deploying AI/ML models
 * Hands-on experience with large language models and agentic AI frameworks (fine-tuning, prompt engineering, multi-agent orchestration, tool use, and API-based production orchestration) required.
 * Proven experience integrating and modeling multimodal datasets (omics, chemical, textual, imaging).
 * Strong software development skills in Python and familiarity with modern ML frameworks (e.g., PyTorch, TensorFlow), MLOps tools, cloud platforms (AWS preferred), and HPC environments.
 * Excellent communication skills; ability to translate complex technical work to domain experts and leadership., * Demonstrated publication record applying AI/ML to life sciences or toxicology.
 * Experience with probabilistic/Bayesian modeling, uncertainty quantification, or causal inference.
 * Prior experience designing agentic systems, human-in-the-loop workflows, or using reinforcement learning for agent behavior control.
 * Prior experience working in regulated environments or developing regulator-ready models., Computational Biology, Computational Chemistry, Data Engineering, Data Modeling, Data Science, Data Visualization, Environmental Toxicology, Foundation Engineering, Large Language Models (LLMs), Machine Learning (ML), Machine Learning Operations, Prompt Engineering, Regulatory Requirements, Software Development, Stakeholder Relationship Management, Toxicology, Uncertainty Quantification, Tooling
Biology, Physics
Planning
Oncology
Research
Medicare
Medicaid
AI Agents
Innovation
Immunology
Statistics
Caregiving
Proteomics
Testability
Probability
Data Science
Communication
Data Analysis
Data Modeling
Deep Learning
Prioritization
Drug Discovery
Modular Design
Histopathology
Medical Records
Version Control
Test Automation
Trustworthiness
Real World Data
Data Processing
German Language
Computer Science
Drug Development
Causal Inference
Machine Learning
Data Engineering
Thought Leadership
Workflow Management
Infectious Diseases
Functional Genomics
Time Off Management
Software Engineering
Statistical Modeling
Technical Leadership
Collective Bargaining
Postdoctoral Research
Computational Biology
Artificial Intelligence
R (Programming Language)
Authorization (Computing)
Verbal Communication Skills
Python (Programming Language)
Medical History Documentation

## Description

Operations
Leadership
Governance
Innovation
Statistics
Compassion
TensorFlow
Toxicology
Ethical AI
Agentic AI
Forecasting
Data Science
Communication
Life Sciences
Data Modeling
Drug Discovery
Bioinformatics
Responsible AI
Agentic Systems
Computer Science
Causal Inference
Machine Learning
Telephone Skills
Data Engineering
Pharmacovigilance
Bayesian Modeling
Data Visualization
Prompt Engineering
Application Design
Workflow Management
Amazon Web Services
Multi-Agent Systems
Software Development
Contingent Workforce
Computational Biology
Reinforcement Learning
Foundation Engineering
Mathematical Chemistry
Artificial Intelligence
Relationship Management
Large Language Modeling
Computational Chemistry
Environmental Toxicology
High Performance Computing
Uncertainty Quantification
Machine Learning Frameworks
Python (Programming Language)
Recurrent Neural Networks (RNNs)
PyTorch (Machine Learning Library)
MLOps (Machine Learning Operations)
Transformer (Machine Learning Model)
Application Programming Interface (API), The Computational Toxicology Group within Nonclinical Drug Safety (NDS) seeks a senior AI/ML scientist to drive the development and deployment of next-generation computational toxicology capabilities. This role will combine advanced machine learning, foundation model engineering, and domain expertise to accelerate safer drug discovery and support regulatory-ready New Approach Methodologies (NAMs). The successful candidate will lead cross-functional projects, deliver production-grade models and agentic systems, and help establish governance and MLOps practices that ensure reproducibility, transparency, and ethical AI use in preclinical research., * Lead deployment of advanced AI/ML solutions (multimodal transformers, graph or sequence models, Bayesian/probabilistic approaches) for toxicity prediction and translational safety applications.

 * Design and implement agentic AI systems tailored to toxicology use cases
 * Specialize in the fine-tuning and alignment of foundation models for toxicology domain-specific applications and supporting new approach methods (NAMs).
 * Drive collaboration with cross-functional teams of toxicologists, computational scientists, biologists, and chemists to ensure explainability, reproducibility, and address specific " context of use " regulatory requirements for safety assessments.
 * Champion best practices in model governance, and responsible AI within a regulated environment, helping to establish frameworks for responsible and ethical AI deployment in preclinical research.
 * Present and communicate science in key internal and external toxicology forums., Tooling
Marketing
Dashboard
Annuities
Test Case
AI Agents
Vertex AI
Claude AI
Leadership
Statistics
Agentic AI
Wholesaling
A/B Testing
Fundraising
AWS Bedrock
Data Science
Azure OpenAI
Communication
Observability
Systems Design
Failure Causes
Tax Accounting
Social Security
Agentic Systems
Computer Science
Causal Inference
Machine Learning
Virtual Training
Value Realization
Business Valuation
Financial Services
Workflow Management
Product Engineering
Retirement Planning
Multi-Agent Systems
Artificial Intelligence
Employment Applications
Applied Machine Learning
Go (Programming Language)
SQL (Programming Language)
Balancing (Ledger/Billing)
Employee Assistance Programs
Business Continuity Planning
Health And Wellness Coaching
Python (Programming Language)
Artificial Intelligence Infrastructure
Application Programming Interface (API)
Applications Of Artificial Intelligence
Machine Learning Model Monitoring And Evaluation, Operations
Leadership
Governance
Innovation
Statistics
Compassion
TensorFlow
Toxicology
Ethical AI
Agentic AI
Forecasting
Data Science
Communication
Life Sciences
Data Modeling
Drug Discovery
Bioinformatics
Responsible AI
Agentic Systems
Computer Science
Causal Inference
Machine Learning
Telephone Skills
Data Engineering
Pharmacovigilance
Bayesian Modeling
Data Visualization
Prompt Engineering
Application Design
Workflow Management
Amazon Web Services
Multi-Agent Systems
Software Development
Contingent Workforce
Computational Biology
Reinforcement Learning
Foundation Engineering
Mathematical Chemistry
Artificial Intelligence
Relationship Management
Large Language Modeling
Computational Chemistry
Environmental Toxicology
High Performance Computing
Uncertainty Quantification
Machine Learning Frameworks
Python (Programming Language)
Recurrent Neural Networks (RNNs)
PyTorch (Machine Learning Library)
MLOps (Machine Learning Operations)
Transformer (Machine Learning Model)
Application Programming Interface (API)
+0


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