AI/ML Engineer, Knowledge Graph

Key Skills

Python Programming, C++, Machine learning techniques, Deep Learning, Keras software library, Natural Language Processing (NLP), PyTorch, TensorFlow, AI technology

Job Description

We are seeking to grow our team with brilliant and diverse contributors with technical ability. We are looking for machine learning and natural language processing experts who are excited by challenges in automated knowledge base construction, including information extraction, entity resolution, graph-based embedding/prediction, and search. This role will involve leveraging state-of-the-art models for these tasks, as well as developing new methods to derive actionable data for scientists. This is an exciting role that will stretch your knowledge and curiosity, offering the opportunity to learn new skills and work within a global community.

This is a hands-on position where you will be empowered to be creative, ambitious and bold, to solve novel R&D problems and have the potential to directly impact the lives of patients living with disease. We have impressive toolkits and world class data, and we are now looking for talented people to join us.

Competitive candidates will have a solid knowledge and experience in machine learning. They should be capable to design, implement and iterate on AI/ML algorithms by quickly learning new models and analyzing the results. Graduate level of education or professional work experiences on NLP, knowledge representation, especially in the biomedical domains are strongly preferred.

The AI/ML team is built on the principles of ownership, accountability, continuous development, and collaboration. We hire for the long term, and we're motivated to make this a great place to work. Our leaders will be committed to your career and development from day one.

As a Machine Learning Engineer, we’d like you to be able to:

• Partner with our NLP team and various data/computing platform teams to construct a knowledge graph of biological, chemical, and medical concepts, representing scientific literature and internal/external experimental findings
• Implement cutting-edge algorithms for inferring likely connections in the knowledge graph for downstream experimental validation
• Design and implement a framework for evaluating the quality and downstream impact of different algorithms for inferring new relationships in the biomedical knowledge graph
• Devise systems for leveraging knowledge graph information within biomedical machine learning problems

Why You?

Basic Qualifications:

• Masters Degree or PhD in computer science, machine learning, data science or related fields, with knowledge and experience on knowledge representation.
• Proficiency in one or more programming languages, including Python, and knowledge of fundamental software engineering principles and NLP/machine learning design patterns.
• Experience with at least one Deep Learning framework such as PyTorch, TensorFlow, or Keras.
• The ability to extract insights from data.
• Excellent written and verbal communication skills.

Preferred Qualifications:

• Refereed publications in premiere NLP, ML, and/or bioinformatics venues in one or more areas related to information extraction and/or knowledge representation.
• Knowledge of distributed learning, big data and related frameworks such as Spark, Kafka.
• Familiarity with biomedical terminology and ontologies (e.g. UMLS Metathesaurus, OMIM), and biomedical datasets such as DrugBanks, GO.

Req ID: 240263