✦ Luna Orbit — AI & Machine Learning

Applied AI Engineer, Life Sciences (Beneficial Deployments)

at Anthropic

📍 San Francisco, CA | New York City, NY Hybrid Posted March 07, 2026
Type Not Specified
Experience mid
Exp. Years 4+ years
Education Not specified
Category AI & Machine Learning

Anthropic is seeking an Applied AI Engineer to develop and deploy AI systems in the life sciences sector, partnering with research institutions to accelerate scientific progress through scalable infrastructure and reusable tools.

  • Partner with research institutions
  • Build reusable infrastructure
  • Deploy AI systems in life sciences
  • Identify deployment challenges
  • Create technical documentation

The role involves building AI deployment systems, managing ML data, and creating infrastructure for scientific research workflows, primarily using Python and related tools.

The ideal candidate is a software engineer with at least 4 years of experience in building AI systems for scientific research, particularly in life sciences. They have strong collaboration skills and experience deploying AI solutions in research environments.

4+ years as a Software Engineer or similarproduction experience shipping systemsPython or Javabuilding systems for research workflows
ML data managementstreaminglow latency data serving
MCP serversgenomics platformsliterature databases
PythonMachine LearningML data managementstreamingAIresearch workflowssystem building
PythonPython (programming language)Machine LearningML data managementstreamingAIreusable agent skillsbenchmarksgenomicsliterature databases
partnershipscientific workflow understandingbuilding prototypescollaborationproblem-solving
Industry SaaS
Job Function Develop and deploy AI solutions for life sciences research
Applied AI EngineerLife SciencesClaudeAIMachine LearningML data managementstreamingreusable agent skillsbenchmarksgenomicsliterature databasesscientific workflowsresearch pipelinesAI deploymentsystem buildingApplied AIresearch workflows

Less than 4 years of experience, No experience with research workflows or scientific data, Lack of production system shipping experience, No familiarity with AI or ML deployment

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