Salesforce Developer

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

• 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

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The salary range for this role is $129,000.00 - $203,100.00

Merck & Co., Inc., Rahway, NJ, USA, also known as Merck Sharp & Dohme LLC, Rahway, NJ, USA, does not accept unsolicited assistance from search firms for employment opportunities. All CVs / resumes submitted by search firms to any employee at our company without a valid written search agreement in place for this position will be deemed the sole property of our company. No fee will be paid in the event a candidate is hired by our company as a result of an agency referral where no pre-existing agreement is in place. Where agency agreements are in place, introductions are position specific. Please, no phone calls or emails.