AI Research Engineer

Merck Merck · Pharma · Central Bohemian, Czech Republic

This role involves research, assessments, and technical evaluations of AI models, methods, and frameworks to solve problems and enhance company AI capabilities, particularly in generative AI and drug design. The engineer will conduct feasibility studies, create proof-of-concepts, explore AI applications to internal datasets, and support intellectual property creation. They will also develop methodologies and toolkits for AI approaches and contribute to raising AI and data science maturity within the company.

What you'd actually do

  1. Perform research, assessments and technical evaluations of AI models, methods and frameworks to solve problems or derive benefit to company AI capabilities and generative AI program
  2. Conduct feasibility studies and create working proof of concepts for use-cases by exploring applications of AI to internal problems and datasets to de-risk AI engineering delivery
  3. Collaborate with research teams on application of AI in drug design or target identification
  4. Proactively scan and keep up to date with AI research
  5. Support the creation of intellectual property

Skills

Required

  • Artificial Intelligence (AI)
  • Machine Learning (ML)
  • python scientific computing stacks
  • Hands-on code development

Nice to have

  • Generative AI
  • Deep Learning
  • Machine Learning frameworks
  • application of AI in protein design
  • cheminformatic
  • developing and deploying within a cloud based infrastructure and services environment
  • pharmaceutical business areas

What the JD emphasized

  • research
  • assessments
  • technical evaluations
  • AI models
  • methods
  • frameworks
  • proof of concepts
  • exploring applications of AI
  • internal problems
  • datasets
  • drug design
  • target identification
  • AI research
  • intellectual property
  • methodologies
  • benchmarks
  • toolkits
  • artificial intelligence
  • data science maturity

Other signals

  • research
  • assessments
  • technical evaluations
  • AI models
  • methods
  • frameworks
  • proof of concepts
  • exploring applications of AI
  • internal problems
  • datasets
  • drug design
  • target identification
  • AI research
  • intellectual property
  • methodologies
  • benchmarks
  • toolkits
  • artificial intelligence
  • data science maturity