Machine Learning Platform Engineer

2 days ago

Los Angeles CA United States Pearl, Inc. Full time

Pearl is looking for a Machine Learning Platform Engineer with at least 5 years of industry experience. You will be responsible for building and maintaining many of the core applications that power Pearl. You will be working to advance Pearl’s machine learning capabilities in both 2-D and 3-D. You will be working to build and maintain the machine learning infrastructure which supports Pearl's machine learning efforts. This role requires familiarity with AWS and Python. The ideal candidate is a practical full stack engineer with experience or interest in machine learning.

Responsibilities:
  • Design, develop, and maintain new features and improvements for Pearl’s existing Machine Learning applications
  • Interface with the Machine Learning and Product teams to design and build new applications which serve Pearl’s core product set
  • Web-based applications (React/Vue)
  • Work with big data for these Machine Learning applications and write optimized queries for the same
  • Work with the Machine Learning team to define and enforce data collection standards
  • Work with Engineering to improve and maintain a rapidly growing datastore
  • Perpetually work to identify and improve usability in all of Pearl’s applications
Minimum Qualifications:
  • B.S., M.S. or Ph.D. degree in Computer Science or a related field or equivalent work experience
  • 5+ years experience in building automation tools, preferably for Machine Learning applications
  • Proven proficiency with Docker
  • Knowledge of SQL and the ability to write efficient, performant queries
  • 3+ years of experience with AWS
  • Experience with open source frameworks like Tensorflow and PyTorch
  • Strong communication skills to effectively collaborate with cross-functional teams including both technical and non-technical team members
Preferred Qualifications :
  • Experience working in medical imaging
  • Experience with CPU/GPU optimization
  • Experience with Kubernetes
  • Meaningful equity in healthtech startup
  • Ongoing learning and development
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