Product Machine Learning Engineer

Added: 15/01/2021

REF: 6990

Contract: Permanent

Location: Boston, Massachusetts, United States

Main duties and responsibilities..

You will contribute to build, improve and deploy our machine learning capability consisting of state of the art NLP solutions for the biomedical domain, automated reasoning over large knowledge graphs and proprietary methods for efficient drug repurposing. You will be following ML best practices and automatically deploy models on both cloud or on-premise specialized hardware. You will be working on the latest trends and methods in the field and publications in major journals/conferences are encouraged.

We are looking for:

  • An advanced degree (masters, PhD) in machine learning or related field with focus on applied research
  • Prior work experience in machine learning, artificial intelligence, natural language processing or related fields
  • Strong experience with major deep learning frameworks: Tensorflow, Pytorch, possibly in a distributed environment.
  • Experience implementing machine learning workflows
  • Strong software development experience (preferably in Python)
  • Experience with distributed data and computing tools like Spark or Apache Beam.
  • Experience or interest in applying deep learning to large graphs.
  • Experience using AWS and/or GCP cloud infrastructure
  • Familiarity with Docker and orchestration services like Kubernetes
  • Publications at major AI conferences.
  • Extremely solid fundamentals in statistical learning methods
  • At least 3 years’ experience in ML engineering
  • Expertise in exploratory analysis and data visualization
  • Expertise in building and supporting large-scale production ML models through multiple iterations.
  • Well-rounded software engineering experience, including a variety of technology ecosystems such as Python, Java, and C++.
  • Experience with genomics, digital image analysis, and clinical data analysis is an asset
  • Excellent interpersonal and communication skills
  • Knowledge of software engineering best practices (Agile, Continuous Value Delivery, CI/CD, DevOps, NoOps, PaaS, IaaS, LEAN software, Service Oriented Architecture, cloud computing)

 

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