Quantitative Research Engineer

Description

The Quantitative Research 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.

The Research and Development Engineer is responsible for developing technology that is impactful, focusing on solving real-world problems that will work seamlessly with the end user.

Northwestern Medicine Information Services drives innovative, high-value solutions to transform health care. We are committed to supporting the relentless pursuit of better medicine by providing exceptional service to our patients and guests as well as internal clients across the organization. To ensure excellence, our team goes to extraordinary lengths to ensure that our systems work together seamlessly.

Northwestern Medicine understands that technology plays an integral role in shaping the future of health care. Information Services strategically supports the organization by:

• Leveraging AI, automation, and rollout of advanced cyber controls that support digital transformation strategies
• Implementing advanced technologies in clinical and administrative areas
• Furthering the development of the end user support model to help enhance modern infrastructure

Responsibilities:

• Design software and hardware solutions in a team-based environment.
• Work closely with the end user to support and improve workflows.
• Stay current on development tools, programming techniques and computing equipment; participate in educational opportunities and read professional publications.

Qualifications

Required:

• Bachelor’s degree in Computer Science, Engineering, Mathematics, Physics, or related technical field, or Master’s related to healthcare with some technical background.
• Experience working directly in software development, ML/AI, or healthcare-related field.
• Excellent written and verbal communication skills.
• Comfort meeting with a range of technical and clinical staff.
• Experience and proficiency with:
  • Manipulating a broad range of data types (e.g., text, 2D images, time series data)
  • Traditional ML techniques (gradient descent, decision trees, etc.)
  • High-level machine learning APIs (Pytorch, JAX, Tensorflow)
  • Design and optimization of data pipelines
  • Python (or similar Object-Oriented language) or C.
  • git/Github
  • Containers

Preferred:

• Advanced degree in Computer Science, Engineering, Mathematics, Physics, or related technical field.
• Experience with a range of data types and manipulations:
  • Multimodal unstructured data (text, 2D images, video, 3D/4D tomographic imagery, waveforms, time series, sparse data)
  • Understanding of how to write optimized data queries (SQL, NoSQL, legacy formats)
• ML/AI tools and methods:
  • ML/AI optimization for training (distributed and others) and inference (ONNX)
  • Deep neural network architectures (transformers, convolutional neural networks, recurrent neural networks, etc.)
• Design and optimization of high throughput data pipelines and computer infrastructure:
  • Low-level hardware programming (C, C++, Rust, assembly, Verilog/VHDL)
  • Network design and infrastructure
  • Containerization platforms and orchestration infrastructure (Docker, Kubernetes)
  • Hybrid cloud infrastructure combining on-and-off-prem high-performance computing.
  • Data and infrastructure security
• Domain-specific tooling:
  • Weights & Biases/Tensorboard, unit testing methodologies, management of large code bases.
  • Ability to robustly document solutions, including diagrams, charts, etc.
  • Integrating code base with standardized documentation platforms (Sphinx)
• Healthcare:
  • Health care data experience (PHI/HIPAA, HL7 FHIR)
  • Familiarity with FDA regulation
  • Applied clinical experience.

Equal Opportunity

Northwestern Medicine is an affirmative action/equal opportunity employer and does not discriminate in hiring or employment on the basis of age, sex, race, color, religion, national origin, gender identity, veteran status, disability, sexual orientation, or any other protected status.