Software Development Engineer, Health & Wellness, Health Tech

Amazon Amazon · Big Tech · Seattle, WA · Software Development

Software Development Engineer role focused on architecting and implementing ML systems for health initiatives, integrating genomic, proteomic, and clinical data. The role involves building high-throughput pipelines, low-latency inference services for biological foundation models, and productionizing ML models for tasks like neoantigen prediction, with a strong emphasis on collaboration with researchers and biologists in an early-stage environment.

What you'd actually do

  1. Lead software architecture design for ML and bioinformatics systems that integrate genomic, proteomic, and clinical data.
  2. Develop high-performance APIs for model inference and pipeline orchestration across sequencing, multi-omics, and immunology workloads.
  3. Implement integration of ML models — including biological foundation models and structure-aware predictors — into prototype and production applications.
  4. Ensure efficient integration of selected models for tasks such as neoantigen prediction, MHC binding/presentation, and peptide ranking.
  5. Optimize system performance, scalability, and reliability for high-throughput bioinformatics pipelines and low-latency inference services.

Skills

Required

  • 3+ years of non-internship professional software development experience
  • 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
  • Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution
  • Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
  • Experience programming with at least one software programming language

Nice to have

  • 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
  • Bachelor's degree in computer science or equivalent

What the JD emphasized

  • lead the architecture and implementation
  • ML systems
  • low-latency inference services
  • integrate ML models into production environments
  • turn experimental ideas into deployed systems
  • deeply enough to make sound architectural choices
  • design feedback loops between wet-lab data and model improvement
  • significant influence over technical direction
  • engineering practices
  • ML/bioinformatics stack
  • systems where the bar for correctness is set by biology

Other signals

  • applying machine learning and bioinformatics to health initiatives
  • lead the architecture and implementation of the ML systems
  • high-throughput pipelines for sequencing and multi-omics data
  • low-latency inference services for biological foundation models
  • integrate ML models into production environments
  • work elbow-to-elbow with ML researchers, computational biologists, and immunologists
  • neoantigen prediction, MHC binding and presentation, T-cell response modeling
  • design feedback loops between wet-lab data and model improvement
  • early-stage initiative
  • significant influence over technical direction, engineering practices, and the ML/bioinformatics stack