Senior Scientist, Agentic AI and Machine Learning (pdmb)

Merck · Pharma · CA

Senior Scientist role focused on developing, benchmarking, and deploying agentic AI and Machine Learning solutions within the healthcare domain (Pharmacokinetics, Dynamics, Metabolism, and Bioanalytics). The role involves partnering with scientists, integrating AI/ML into R&D platforms, and contributing to responsible AI practices. Requires hands-on experience with LLMs, agent frameworks, multimodal datasets, and model evaluation.

What you'd actually do

  1. Design, develop, benchmark and deploy AI agents to support PDMB and clinical workflows, including: automated report generation, quality evaluation and consistency checks, process monitoring and deviation detection, scheduling, prioritization, and alerting systems.
  2. Develop and apply machine learning and deep learning models for DMPK and clinical applications, including: Build and evaluate simulation and hybrid ML–mechanistic models to support decision-making in discovery and development and apply best practices in model validation, benchmarking, uncertainty estimation, and performance monitoring.
  3. Define benchmarks and success metrics for AI agents and ML models, including scientific quality, operational efficiency, and user adoption.
  4. Integrate agents and ML methods into existing R&D platforms, laboratory systems, data lakes, and clinical data environments.
  5. Contribute to responsible AI practices, including transparency, reproducibility, governance, and compliance with GxP considerations.

Skills

Required

  • Python
  • ML frameworks (PyTorch, TensorFlow)
  • MLOps tools
  • cloud platforms (AWS preferred)
  • HPC environments
  • large language models
  • agentic AI frameworks
  • fine-tuning
  • prompt engineering
  • multi-agent orchestration
  • tool use
  • API-based production orchestration
  • integrating and modeling multimodal datasets
  • model evaluation
  • benchmarking
  • stakeholder management
  • influencing without authority
  • cross-functional teams
  • communication skills

Nice to have

  • computational chemistry
  • bioinformatics

What the JD emphasized

  • agentic AI
  • Machine Learning
  • large language models and agentic AI frameworks (fine-tuning, prompt engineering, multi-agent orchestration, tool use, and API-based production orchestration) required
  • integrating and modeling multimodal datasets (omics, chemical, textual, imaging)
  • model evaluation and benchmarking

Other signals

  • Agentic AI
  • Machine Learning
  • Drug Development
  • LLMs
  • Healthcare