Arterys is working on applying artificial intelligence to create algorithms that will impact the lives of millions of people worldwide. We are looking for passionate and talented people with expertise in software engineering and enthusiasm for machine learning to advance the field of medicine.

Much of radiology consists of performing tedious tasks that would greatly benefit from automation, such as segmenting anatomical regions, characterizing lesions and writing reports. Additionally, non-radiologist clinicians who make treatment decisions often do not have the ability to synthesize the myriad components of available clinical information to make the best possible treatment decisions for their patients. Arterys’ goal is to use the latest machine learning technology to solve these problems, help clinicians work more efficiently, and make a dramatic impact on patient outcomes.

As a Machine Learning Engineer at Arterys, you will play a critical role in building, monitoring, and maintaining internal tools and infrastructure used for data management, model training, performance optimization and everyday machine learning operations. You are passionate about correct, performant, maintainable code that keeps our machine learning development running with minimal downtime. You’ll develop elegant and efficient solutions to messy, real-world problems and you’ll have significant impact on the Arterys machine learning team’s ability to quickly develop and deliver effective models.

Responsibilities

  • Develop pipelines for data processing and deep learning model training
  • Develop and support cloud training infrastructure (e.g., AWS, GCP, Azure)
  • Develop and support data and model cataloging
  • Optimize training efficiency via parallelization, caching and merciless low-level hacking
  • Keep our cloud costs under control
  • Work with Arteys production DevOps team to ensure systems are scalable and secure

Requirements

  • 3+ years of production software experience in industry
  • Experience with unit testing, continuous integration and design documentation, particularly with Python
  • Focus on efficient, well tested, easily maintainable code
  • Experience building scalable and distributed cloud infrastructure with Kubernetes or similar technologies
  • Experience with SQL and SQL-backed database architecture (SQLite, MySQL, etc.); NoSQL database experience (e.g., MongoDB) is also helpful
  • Well-versed in Linux systems administration and architecture

Nice to Haves

  • B.S. or higher in computer science or a related engineering discipline
  • Practical knowledge of front-end application development
  • Experience with medical data, particularly the DICOM standard
  • Knowledge of machine learning and deep learning, particularly convolutional neural networks and/or recurrent neural networks for natural language processing
  • Experience working with modern deep learning packages, particularly Keras and/or Tensorflow
  • Experience with handling and processing large datasets (1 TB and larger)

One of our goals in developing safe and effective medical technology is to ensure that all individuals, including those who may come from socially disadvantaged populations, have access to the same high quality healthcare. To advance this goal, we feel it is important to recruit a diverse workforce. We therefore encourage women and other underrepresented minorities to apply to this position to ensure that a diverse set of experiences and views is represented within our team.

Arterys is an Equal Opportunity Employer committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. Reasonable accommodations may be made to enable individuals with disabilities to perform essential job functions.

Please e-mail your resume and a short cover letter that includes a description of your distributed software development experience to ai-careers@arterys.com. Please include “Machine Learning Engineer” in the subject line.