Overview: Reboot Rx is a nonprofit startup that enables cancer patients to access new and affordable treatments faster than traditional approaches using repurposed generic drugs. Hundreds of available, safe, and inexpensive non-cancer generic drugs could be repurposed for treating cancer. We are leveraging AI technology and innovative funding models to find the most promising repurposing opportunities and generate the necessary evidence for them to be used as cancer treatments. Working with us is a great opportunity to get hands-on experience at a cutting-edge social impact startup at the intersection of AI, biology, and medicine.
Details: This internship is for the winter/spring session. You should be passionate about expanding treatment options for cancer patients, a hardworking self-starter with attention to detail, and effective at both independent and collaborative work. You will work directly with the founders (https://rebootrx.org/team) and will interact with other members of the highly cross-disciplinary team. A commitment of 12-20 hours per week for at least 10-12 weeks is preferred. The team currently operates out of Boston, MA and Portland, ME. Due to the ongoing COVID-19 restrictions, the work will be conducted remotely with regular virtual meetings and communications. We operate in U.S. Eastern Time, but are flexible to accommodate other time zones. This is an unpaid nonprofit internship/volunteer opportunity. Interested students are strongly encouraged to apply for funding or class credits through their school.
To Apply: First learn more about Reboot Rx on our website (https://rebootrx.org). Then email Devon Crittenden (firstname.lastname@example.org) your resume and a short explanation of why you are interested in working with Reboot Rx. Please clearly state the position(s) you are interested in. Applicants will be reviewed on a rolling basis.
Machine Learning Engineer (NLP, data engineering)
- Build, test, and deploy NLP pipelines to extract features from scientific literature to handle language-based tasks (named entity recognition, parsing, classification)
- Research and implement methods to improve oncology specific terminology for named entity recognition (NER) tasks
- Optimize language models and algorithms for relationship extraction and entity linking
- Work on feature engineering, cluster analysis, and implementation of novel statistical methods to improve model performance
- Develop and scale backend data infrastructure to manage, extract, and store data deployed on AWS
- Design and develop applications to visualize predictions, insights, and model performance
Preferred skills and qualifications
- Pursuing a computer science, engineering, or other quantitative degree
- Experience with ML algorithms and packages such as scikit-learn, spaCy, Gensim, TensorFlow, PyTorch, Snorkel, StanfordNLP, etc.
- Familiarity with NLP models (LSTMs, Transformers) and techniques (sentence embeddings, topic modeling)
- Strong coding skills in python and experience implementing web frameworks such as Flask or Django
- Knowledge of database languages (SQL, MongoDB) is desirable
- Experience working with and deploying models to one of the following cloud environments: AWS, GCP, Azure
- Knowledge of life science literature and biomedical data