Job description

The Machine Learning (ML) Engineer reflects the mission, vision, and values of NM, adheres to the organization’s Code of Ethics and Corporate Compliance Program, and complies with all relevant policies, procedures, guidelines and all other regulatory and accreditation standards.

ML Engineers are responsible for building and managing the software and infrastructure of ML operations within NM’s Digital Platform. Given the highly interdisciplinary and cutting edge nature of the engineering tasks performed by the entire team, infrastructure and software are tightly interwoven so a highly specialized staff comfortable with both areas is necessary.


  • Building high performance data pipelines to connect systems (i.e. ADLS, SQL, Synapse, DICOM) to ML training hardware on-cloud
  • Performing low-level system performance analysis using tracing software to identify performance bottlenecks of custom pipelines
  • Actively participate in design discussions with end users of pipelines (data scientists) and of the algorithms built from these pipelines (physicians, nurses, other providers)
  • Documentation and presentation of infrastructure in internal and external forums, including open-source standards
  • Routine and spot maintenance of existing pipelines and infrastructure
  • Deploying/maintaining container instances on a Kubernetes platform



  • Bachelor’s degree in comp engineering/comp sci/math/applied math/statistics or related
  • Three to five years’ experience deploying, maintaining, monitoring integrated ML/AI in a production setting
  • Experience with low-level programming (C, assembly)
  • Experience with hardware programming (embedded C, CUDA, Verilog/VHDL)
  • Experience with high-level, high throughput APIs (Tensorflow, Pytorch, OpenMPI, ONNX)
  • Experience with high-throughput data pipelines (SSIS, Azure Data Factory)
  • Experience with high-performance containerization platforms (Docker, Singularity)
  • Experience with hybrid container orchestration infrastructure (Kubernetes)


  • Knowledge of Healthcare data (EMR, Claims, Billing, HL7, FHIR), HIPAA/PHI, PII and data security considerations with generalized transformer architecture.
  • Strong background in Data Architecture/Computer Science/Computer Engineering

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