Principal ML Research Engineer - Elucid

Are you working in a job that is making a difference for millions of people? If not, keep reading to learn more about Elucid. 

We are a rapidly growing, Boston-based medical technology company using AI to help clinicians make more informed decisions on cardiovascular care - leading to better patient outcomes, improved quality of care, and reduced healthcare costs. Come work at Elucid and be part of delivering on our mission to prevent heart attacks and strokes.

*** While the position is listed in Boston, MA we are accepting resumes from candidates in the continental US, eastern time zone preferred ***

We’re looking for a Principal ML Research Engineer to help build and extend this unique, revolutionary product. This role is ideal for experienced candidates with a background in biomedical image understanding and interest in ML/DL for healthcare generally. You will work with modern DL techniques (e.g. self- and semi-supervised learning, transformers, novel architectures, advanced hyperparameter search) and frameworks (PyTorch), and help implement our models on our powerful GPU cluster.

Responsibilities:

• Help build the data pipeline, including writing data curation algorithms and data loaders.
• Collaborate with clinical team members to ensure data quality and model validity.
• Create, test, and optimize deep learning models.   
• Integrate deep learning models into a production environment.

The ideal candidate will have:

• PhD in machine learning, computer science, mathematics, statistics or related quantitative field.
• 6 years overall experience developing ML algorithms. 
• 2 years Python experience.
• At least 3 years of prior experience developing ML applications in industry, especially in the computer vision or biomedical image understanding (preferred) space. This work has resulted in publications, been integrated into product, or similar. 
• Familiarity with Python-based deep learning frameworks (ideally PyTorch, but others ok too).
• Familiarity with modern MLOps tools like Weights & Biases, MLFlow, Neptune, etc. 
• Familiarity with git and medium- to large-scale software development.

Preferred Experience, not required: 

• Experience working in an FDA-regulated environment.
• Experience with optimization, e.g. Bayesian Optimization. 
• AWS experience (EC2, S3, Sagemaker).
• C coding experience.
• Experience with VTK/ITK.
• Experience with Systems Biology models.

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